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1 INTRODUCTION

Global navigation satellite systems (GNSSs) constitute

a fundamental component of modern positioning,

navigation, and timing (PNT) infrastructures. They

underpin a wide spectrum of civilian and military

applications, including aviation and maritime

navigation, land transportation, telecommunications

and financing sector synchronization, power-grid

monitoring, and precision-guided military operations.

The ubiquitous reliance on GNSS-derived PNT

information has, however, exposed a critical

vulnerability: the extreme susceptibility of GNSS

signals to intentional and unintentional radio-

frequency (RF) interference [1].

GNSS signals received at the Earth’s surface are

characterized by very low power levels, typically well

below the thermal noise floor. As a result, even low-

cost and low-power transmitters are capable of

significantly degrading GNSS reception. Among

intentional interference methods, jamming and

spoofing represent the most serious and operationally

relevant threats. Jamming aims to deny GNSS service

by overwhelming receivers with interference, while

spoofing seeks to deceive receivers by broadcasting

GNSS Jamming and Spoofing Situational Awareness

Maps

D. Zmysłowski & J.M. Kelner

Military University of Technology, Warsaw, Poland

ABSTRACT: The widespread and rich use of global navigation satellite systems (GNSSs) means that they have

become a frequent target for intentional interference that impedes, falsifies, or prevents their operational

existence. Such activities are related to deliberate disinformation actions or limiting access to services essential

for transport, critical infrastructure, government, civil services, or military. They are usually an element of

asymmetric hybrid actions carried out as part of information warfare conducted in the electromagnetic spectrum

using electronic warfare (EW) techniques, terrorist operations, or are related to human carelessness. In military

operations, they constitute a means of influencing navigation warfare (NAVWAR). When GNSS is caused by

jamming or spoofing, it becomes challenging to use effectively. Utilizing their services introduces significant

uncertainty, as it is unclear whether the provided values for position, speed, and time are reliable. Nowadays,

many incidents are observed concerning the jamming of GNSS signals, causing adverse effects on maritime

navigation, air operations, and land transport, the synchronization of telecommunications systems (especially

mobile), as well as disruptions to the operation of energy and financial systems. The scale of these phenomena is

massive, and their scope covers countries (e.g., Ukraine), regions of countries (e.g., Poland, Lithuania, Latvia,

Estonia, Finland, Sweden, and others), also large areas of shipping waters, such as the Baltic Sea, the

Mediterranean Sea, or the Black Sea. In this paper, we present the idea of using situational awareness maps to

visualize and assess the current state of disruptions to the functioning of global systems in each region caused by

jamming or spoofing.

http://www.transnav.eu

the International Journal

on Marine Navigation

and Safety of Sea Transportation

Volume 20

Number 2

June 2026

DOI: 10.12716/1001.20.02.09

336

counterfeit navigation signals, potentially inducing

hazardous position or timing errors without

immediate detection [2], [3], [4], [5].

In recent years, the operational relevance of GNSS

interference has increased markedly due to the

proliferation of portable jammers, software-defined

radios (SDRs), and increasingly sophisticated spoofing

techniques. This evolution has elevated GNSS

interference from a localized nuisance to a strategic

instrument within the broader framework of

navigation warfare (NAVWAR) [6], [7]. In military

contexts, GNSS disruption can directly impair

command and control, precision fires, unmanned

systems, and synchronized communications. In

civilian domains, documented interference incidents

have demonstrated their capacity to affect airport

operations, maritime safety, emergency services, and

other elements of critical infrastructure.

Ensuring resilience against such threats requires

more than robust receiver design alone. It necessitates

the ability to observe, interpret, and respond to the

dynamic GNSS interference environment in near real

time. This capability is commonly referred to as GNSS

situational awareness (GNSS-SA) [8], [9]. GNSS-SA

encompasses the continuous monitoring of GNSS

signal performance and integrity, the detection and

classification of anomalies, and the contextual

interpretation of these observations within an

operational and geographic framework.

A key enabler of GNSS-SA is the GNSS situational

awareness map (GNSS-SAM). GNSS-SAM can be

understood as a dynamic, georeferenced visualization

environment that integrates heterogeneous GNSS

performance metrics and interference indicators

collected from distributed sensor networks. By fusing

data from reference GNSS stations, mobile platforms,

specialized interference monitoring sensors, and

crowd-sourced receivers, GNSS-SAM provides a

spatially coherent representation of GNSS service

quality and threat conditions. This map-based

paradigm transforms raw signal-level measurements

into actionable situational awareness products,

supporting timely mitigation actions and informed

operational decision-making.

The objective of this paper is to present a

comprehensive overview of GNSS jamming and

spoofing situational awareness maps, with particular

emphasis on the GNSS-SAM concept. The paper

discusses the architectural foundations and functional

objectives of GNSS-SAM systems, the data sources and

signal-level features used for interference detection

and classification, and the role of data fusion and

geospatial visualization in enhancing situational

awareness. Selected application scenarios from

aviation, maritime operations, telecommunications,

and security and defense domains are examined to

illustrate the operational value of GNSS-SAM-based

approaches.

Several GNSS-SA–oriented solutions have already

been proposed in the literature; however, these

approaches typically address only selected aspects of

the overall problem and do not provide a fully

integrated, multi-layer situational awareness

framework. For example, aerial mapping solutions

based on unmanned aerial vehicles have been

demonstrated as effective tools for detecting and

localizing GNSS interference sources over large areas,

offering high spatial resolution and flexibility, but they

are inherently episodic and platform-dependent rather

than continuously operating monitoring systems [10].

Crowdsourcing-based approaches leveraging

smartphone applications have shown promise in

enabling large-scale, real-time mapping of GNSS

interference with minimal infrastructure cost;

nevertheless, such methods are constrained by

heterogeneous receiver quality, limited signal

observables, and reduced detection reliability [11].

More comprehensive monitoring services, such as

national GNSS-SA platforms based on permanent

reference station networks, can provide continuous

signal quality assessment and leverage advanced

services such as Galileo Open Service Navigation

Message Authentication (OSNMA) and the High

Accuracy Service (HAS). The Finnish Geospatial

Research Institute (FGI) has implemented an open-

access GNSS-SA platform, referred to as GNSS-

Finland, which provides continuous monitoring of

GNSS signal conditions. The system assesses signal

quality, identifies indications of interference, and

delivers information on the anticipated performance of

GNSS services based on observations from

approximately 47 stations belonging to the Finnish

continuously operating reference station (CORS)

network. However, these systems remain

geographically limited and are often tailored to specific

constellations or services rather than addressing GNSS

interference awareness in a fully global and multi-

domain sense [12]. These examples highlight the need

for a more holistic GNSS-SAM concept capable of

integrating heterogeneous data sources, detection

techniques, and visualization layers into a unified

operational picture.

By consolidating existing concepts and practical

solutions into a coherent framework, this work aims to

contribute to the development of resilient GNSS

monitoring, interference awareness, and navigation

assurance strategies applicable to both civilian and

military environments.

The main contribution of this paper is the

introduction of a unified GNSS-SAM conceptual

framework integrating heterogeneous sensor

networks, interference indicators, geospatial

visualization techniques, and operational awareness

mechanisms into a coherent architecture supporting

resilient PNT monitoring.

The remainder of this paper is organized as follows.

Section 2 discusses the inherent vulnerabilities of

GNSS and the principal interference threats arising

from jamming and spoofing. Section 3 introduces the

concept of GNSS SA and defines the role of the GNSS

SAM. Section 4 outlines the key objectives and

functional requirements of GNSS SAM systems, while

Section 5 describes the data sources and sensor

networks supporting GNSS SAM operation. Section 6

focuses on signal features and interference indicators,

and Section 7 presents data fusion and anomaly

classification approaches applied within GNSS SAM

frameworks. Section 8 addresses geospatial

visualization aspects and overall GNSS SAM

architecture. Section 9 reviews existing GNSS SAM

solutions, and Section 10 discusses representative

application scenarios. Section 11 provides a discussion

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of current challenges and future development

directions, and Section 12 concludes the paper.

2 GNSS VULNERABILITIES AND INTERFERENCE

THREATS

2.1 Characteristics of GNSS Signals and Inherent

Vulnerabilities

GNSSs are based on the reception of spread-spectrum

radio signals transmitted by satellites operating at

medium Earth orbit altitudes of approximately 20,000

km. Due to the long propagation distance and limited

satellite transmit power, GNSS signals arrive at the

user receiver with extremely low power levels,

typically on the order of −160 dBW to −130 dBW. These

signal levels are often well below the thermal noise

floor of the receiver front end, requiring sophisticated

correlation and integration techniques for reliable

signal acquisition and tracking [1].

This fundamental signal weakness constitutes the

primary source of GNSS vulnerability. Any additional

radio-frequency energy present within the GNSS

bands, whether unintentional or deliberate, can

significantly degrade receiver performance. Even

narrowband interference affecting only a small portion

of the spectrum may disrupt code or carrier tracking

loops, leading to loss of lock, increased measurement

noise, or biased pseudo-range estimates. The reliance

on open, publicly documented signal structures further

amplifies the exposure of civil GNSS signals to

exploitation [13], [14].

Moreover, GNSS receivers typically operate under

the assumption of benign signal environments. While

modern receivers incorporate interference mitigation

features, many mass-market and legacy systems lack

robust detection mechanisms for sophisticated threats.

As a result, performance degradation may remain

unnoticed until navigation or timing errors reach

operationally critical levels. These inherent

vulnerabilities form the technical foundation upon

which both jamming and spoofing attacks are built

[13], [15].

2.2 Jamming Techniques and Effects

GNSS jamming refers to the intentional transmission of

RF signals designed to disrupt the reception of

legitimate GNSS signals. The simplest form of jamming

employs continuous-wave (CW) interference centered

on a GNSS carrier frequency. Despite its simplicity,

CW jamming can be highly effective, particularly

against receivers with limited front-end filtering or

adaptive notch capabilities. More advanced jammers

employ broadband or chirp-like waveforms to cover

wider frequency ranges and affect multiple GNSS

constellations simultaneously [4], [16].

From an operational perspective, jamming

primarily results in denial-of-service attacks. Receivers

exposed to sufficiently strong interference experience a

rapid reduction in carrier-to-noise density ratio (C/N₀),

followed by loss of satellite tracking and invalid

navigation solutions [17], [18]. Timing receivers may

experience holdover mode activation or, in severe

cases, complete loss of synchronization. As highlighted

in the presentation material, sudden C/N₀ drops

exceeding 10 dB across multiple receivers represent a

strong indicator of jamming activity [15].

Jamming attacks are particularly attractive due to

their low technical barrier and cost-effectiveness.

Portable jammers, often powered by vehicle batteries,

can create disruption zones spanning several

kilometers, especially in open environments such as

maritime or rural areas. While jamming is relatively

easy to detect, its mitigation remains challenging in

real time, reinforcing the need for distributed

monitoring and situational awareness mechanisms

such as GNSS-SAM systems [11].

2.3 Spoofing Techniques and Threat Scenarios

In contrast to jamming, GNSS spoofing aims not to

deny service, but to manipulate receiver outputs by

transmitting counterfeit GNSS-like signals. These

signals are structured to mimic authentic satellite

signals while conveying false navigation data.

Spoofing attacks range from simple meaconing

(rebroadcasting delayed genuine signals) to highly

sophisticated seamless spoofing, where the victim

receiver is gradually captured without loss of lock [4],

[19], [20].

Spoofing poses a particularly dangerous threat

because it can remain covert. A receiver under

spoofing attack may continue to report valid position

and time solutions that appear internally consistent,

while being significantly offset from reality. In timing-

dependent infrastructures such as telecommunications

networks or power grids, even microsecond-level

timing offsets can lead to cascading failures or loss of

synchronization [21].

Detection of spoofing typically relies on indirect

indicators rather than raw signal power levels. As

discussed in the presentation, such indicators include

inconsistencies between satellite elevation and

azimuth angles, correlation function distortions, time-

jump events, and multi-receiver spatial inconsistencies.

The complexity and subtlety of spoofing attacks make

them a central concern for GNSS-SA frameworks and

motivate the integration of multi-sensor and map-

based analysis tools [5], [20], [22], [23].

2.4 Operational Impact of GNSS Interference

The operational consequences of GNSS interference

extend far beyond isolated receiver failures. In

aviation, jamming and spoofing events near airports

can disrupt GNSS-based approach procedures and

increase pilot workload, potentially leading to flight

delays or diversions. Maritime operations face similar

risks, particularly in congested ports and narrow

waterways where precise positioning is essential for

safe maneuvering [24], [25], [26], [27].

In terrestrial and infrastructure domains, GNSS

interference can compromise synchronization in

cellular networks [28], [29], [30] and power grids [21],

[31]. Telecommunications base stations relying on

GNSS timing may experience phase misalignment,

leading to degraded service quality or network

outages. Power systems using phasor measurement

units (PMUs) are particularly sensitive to timing errors,

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which can undermine grid stability during dynamic

operating conditions [32], [33].

From a military perspective, GNSS interference is

an integral element of NAVWAR [6], [7]. Disrupting

GNSS-based PNT can degrade command and control,

precision-guided munitions, unmanned systems, and

coordinated maneuvers. These wide-ranging impacts

underscore the necessity of continuous, geospatially

aware monitoring solutions capable of correlating

interference effects across domains, precisely the role

envisioned for GNSS-SAM architectures.

3 GNSS SITUATIONAL AWARENESS CONCEPT

3.1 Definition of GNSS Situational Awareness (GNSS

SA)

GNSS-SA can be broadly understood as the capability

to perceive, comprehend, and anticipate the

operational state of GNSS services within a given

spatial and temporal context. Unlike traditional

performance monitoring, which focuses primarily on

receiver-level metrics, GNSS-SA emphasizes system-

level understanding by correlating observations from

multiple sensors and platforms. This holistic

perspective is essential in environments where GNSS

signals are subject to dynamic interference, intentional

attacks, or environmental disturbances.

At its core, GNSS-SA builds upon continuous

observation of GNSS signal characteristics such as

C/N₀, tracking stability, pseudo-range consistency, and

timing integrity. However, raw measurements alone

are insufficient to provide meaningful awareness.

GNSS-SA requires contextualization of these

measurements, including knowledge of the receiver

location, satellite geometry, operational environment,

and temporal evolution of observed anomalies. Only

through such contextual integration can deviations

from nominal behavior be correctly interpreted.

An important aspect of GNSS-SA is its ability to

discriminate between benign degradations and hostile

actions. Natural phenomena such as ionospheric

scintillation, multipath in urban environments, or

satellite maintenance events may produce symptoms

similar to deliberate interference. Therefore, GNSS-SA

frameworks rely on comparative analysis across

geographically distributed sensors to identify patterns

that are inconsistent with natural causes, such as

abrupt spatial boundaries or simultaneous multi-

frequency disruptions.

Finally, GNSS-SA should be viewed as a decision-

support capability rather than a standalone detection

mechanism. Its ultimate purpose is to enable timely

and informed responses, such as switching to

alternative navigation or timing sources, issuing

operational warnings, or initiating countermeasures.

This aligns GNSS-SA conceptually with situational

awareness paradigms used in air traffic management,

maritime domain awareness, and cybersecurity

operations [9].

3.2 GNSS Situational Awareness Map (GNSS SAM):

Definition and Scope

The GNSS-SAM represents a practical and intuitive

realization of the GNSS-SA concept. GNSS-SAM can be

defined as a dynamic, georeferenced dashboard that

integrates heterogeneous GNSS performance data (i.e.,

signal metrics, interference reports, integrity monitors)

from distributed sensor nodes, processes these data

through analytics and anomaly‑detection algorithms,

and presents them as layered geographic information

for decision‑makers to assess and respond to GNSS

threats or degradations. By projecting these data onto

a geographic map, GNSS-SAM enables operators to

rapidly assess the spatial extent, severity, and

evolution of GNSS degradations.

In contrast to conventional dashboards or tabular

monitoring tools, GNSS-SAM leverages spatial

cognition to enhance human understanding of

complex interference scenarios. Color-coded

performance layers, event markers, and temporal

animations allow users to identify interference

‘hotspots,’ movement of jamming sources, or regions

affected by spoofing. This spatial abstraction is

particularly valuable when monitoring wide-area

infrastructures such as airspace sectors, maritime

regions, or national communication networks.

The scope of GNSS-SAM extends beyond real-time

visualization. As emphasized in the presentation,

GNSS-SAM systems typically incorporate historical

data archiving, enabling post-event forensic analysis

and long-term trend assessment. Such capabilities

support the identification of recurring interference

patterns, assessment of adversary tactics, and

evaluation of mitigation effectiveness. In this sense,

GNSS-SAM functions both as an operational tool and

as an analytical platform.

It is also important to note that GNSS-SAM is

inherently scalable. Depending on the application, it

may operate at local, regional, or global levels,

integrating data from a handful of specialized sensors

or thousands of heterogeneous receivers. This

flexibility allows GNSS-SAM architectures to be

adapted to diverse civilian and military use cases, from

port-level monitoring to national or multinational

GNSS interference awareness initiatives [34], [35].

3.3 GNSS-SAM in Context of Navigation Warfare

(NAVWAR)

Within the NAVWAR framework, GNSS-SAM plays a

role analogous to that of a common operational picture.

NAVWAR encompasses deliberate actions intended to

degrade, deny, or deceive an adversary's navigation

capabilities, with GNSS a primary target due to its

central role in modern PNT architectures. In this

context, GNSS-SAM provides commanders and

operators with visibility into the navigation domain,

which has traditionally been less observable than

physical or cyber domains.

GNSS-SAM enables early detection of hostile

NAVWAR activities by correlating anomalies across

multiple receivers and platforms. For example,

simultaneous loss of GNSS service across

geographically separated units may indicate wide-area

jamming, while coherent position deviations among

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mobile assets may suggest coordinated spoofing. By

presenting such information in a unified geospatial

view, GNSS-SAM supports rapid threat assessment

and response coordination.

Beyond detection, GNSS-SAM contributes to

operational resilience by informing mitigation

strategies. Knowledge of interference boundaries and

severity allows operators to reroute platforms, adjust

mission planning, or activate alternative navigation

and timing sources. In military environments, GNSS-

SAM outputs may also be fused with intelligence,

surveillance, and reconnaissance (ISR) data to support

attribution and targeting of interference sources.

Finally, the integration of GNSS-SAM into

command-and-control systems reflects a broader shift

toward treating PNT as a contested and managed

resource rather than an assured utility. As NAVWAR

capabilities continue to evolve, GNSS-SAM

architectures are expected to play an increasingly

important role in maintaining operational effectiveness

across all domains, a trend clearly aligned with the

motivations outlined in the accompanying

presentation [5], [6], [7].

4 OBJECTIVES AND FUNCTIONAL

REQUIREMENTS OF GNSS-SAM

4.1 Real-Time GNSS Performance Monitoring

A fundamental objective of GNSS-SAM is the

continuous, near real-time monitoring of GNSS signal

performance across a geographically distributed set of

sensors. Unlike post-processed quality assessment,

real-time monitoring enables immediate awareness of

abnormal conditions that may indicate interference,

system misconfiguration, or environmental

disturbances. This requirement is particularly critical

in safety-of-life and mission-critical applications,

where delayed awareness may translate directly into

operational risk.

Real-time monitoring within GNSS-SAM typically

relies on the collection of low-level signals and

navigation observables, such as C/N₀, satellite tracking

status, pseudo-range residuals, and receiver position

or timing consistency. As highlighted in the

presentation, sudden, correlated deviations in these

metrics, especially when observed across multiple

sensors, serve as early indicators of GNSS service

degradation. The system must therefore support high-

rate data ingestion and low-latency processing

pipelines.

Another important functional requirement is

temporal coherence. GNSS-SAM must ensure precise

time alignment of measurements originating from

heterogeneous sensors, often operating under different

clocks and network conditions. Accurate

timestamping, buffering, and synchronization are

prerequisites for meaningful cross-sensor correlation,

particularly when detecting fast-evolving interference

phenomena such as pulsed jamming or transient

spoofing attempts [4].

Finally, scalability is essential for real-time

monitoring. GNSS-SAM architectures should be

capable of handling anything from a small regional

sensor network to thousands of distributed receivers

without compromising responsiveness. This

requirement strongly influences system design choices

for data transport, processing architectures, and cloud-

or edge-based deployment models [13].

4.2 Interference Detection and Classification

Beyond raw monitoring, GNSS-SAM is required to

detect and classify interference events in an automated

and reliable manner. Detection refers to identifying

deviations from nominal GNSS behavior, while

classification aims to determine the nature of the

underlying cause, such as jamming, spoofing, or

benign environmental effects. These functions form the

analytical core of GNSS-SAM systems.

Detection mechanisms are typically based on

thresholding, statistical change detection, or pattern

recognition applied to signal-level indicators. For

example, abrupt and broadband C/N₀ degradation

across multiple frequencies may indicate jamming,

whereas subtle distortions in correlation functions or

inconsistencies in satellite geometry may point toward

spoofing [23]. The presentation emphasizes that no

single indicator is sufficient, reinforcing the need for

multi-feature analysis.

Classification requires the fusion of multiple

indicators over time and space. Rule-based approaches

remain attractive due to their transparency and ease of

validation, particularly in regulated environments.

However, as interference scenarios become more

complex, machine learning (ML) techniques (trained

on labeled interference data) are increasingly explored

to enhance discrimination performance [36], [37], [38].

GNSS-SAM should support both approaches, allowing

gradual evolution of detection logic.

An important functional requirement is robustness

against false alarms. Excessive false positives can

undermine operator trust and reduce the operational

value of GNSS-SAM. Therefore, detection and

classification modules must incorporate confidence

measures and contextual information, such as known

maintenance events or ionospheric activity, to reduce

ambiguity [5].

4.3 Alerting and Notification Mechanisms

A key operational objective of GNSS-SAM is to

translate detected interference events into timely and

actionable alerts. Alerting mechanisms serve as the

primary interface between the situational awareness

system and its users, enabling rapid response to

emerging GNSS threats. Effective alerting requires

careful balancing between responsiveness and

information overload.

Alerts in GNSS-SAM are typically generated when

predefined thresholds or confidence levels are

exceeded. As illustrated in the presentation, examples

include sudden C/N₀ drops greater than a specified

value, simultaneous anomalies reported by multiple

receivers, or detection of spoofing indicators persisting

beyond a defined duration. These triggers must be

configurable to accommodate different operational

contexts, such as aviation, maritime, or

telecommunications environments.

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Notification mechanisms should support multiple

delivery channels, including visual indicators on the

GNSS-SAM interface, automated messages (e.g., email

or network notifications), and machine-to-machine

(M2M) interfaces for integration with external systems.

In critical infrastructures, alerts may directly trigger

fallback procedures, such as switching to alternative

timing sources or non-GNSS navigation modes.

Equally important is the content of alerts. GNSS-

SAM alerts should convey not only the existence of a

problem, but also its estimated severity, spatial extent,

and confidence level. This enables operators to

prioritize responses and avoid unnecessary

operational disruptions [34], [35].

4.4 Historical Data Analysis and Forensic Capabilities

In addition to real-time functions, GNSS-SAM must

support historical data storage and post-event analysis.

Archiving past measurements and detected events

enables forensic investigations, trend analysis, and

system performance evaluation. This capability is

particularly valuable for understanding recurring

interference patterns and assessing long-term GNSS

resilience.

Historical analysis allows operators to correlate

GNSS interference events with external factors such as

geographic location, time of day, or known activities.

For example, repeated jamming incidents near specific

infrastructure or borders may indicate persistent

interference sources. GNSS-SAM systems should

therefore support efficient querying, visualization, and

replay of historical data [39].

From a system development perspective, archived

data also serve as an essential resource for improving

detection and classification algorithms. Labeled

historical events can be used to refine thresholds,

validate rule-based logic, or train ML models. This

feedback loop supports continuous improvement of

GNSS-SAM performance over time.

Finally, forensic capabilities contribute to

accountability and reporting. In regulated domains

such as aviation or critical infrastructure protection,

documented evidence of GNSS interference may be

required for incident reporting, regulatory compliance,

or coordination with national authorities. GNSS-SAM

thus functions not only as a real-time awareness tool,

but also as a long-term knowledge repository [40].

5 GNSS-SAM DATA SOURCES AND SENSOR

NETWORKS

5.1 Fixed GNSS Reference Stations

Permanent GNSS reference stations constitute one of

the most reliable and widely used data sources for

GNSS-SAM systems. These stations operate at known,

precisely surveyed locations and provide continuous

observations of GNSS signals with high temporal

stability. Their fixed geometry and long-term operation

make them particularly suitable for detecting

persistent or wide-area interference affecting GNSS

service quality.

GNSS-SAM systems can leverage data from

existing national and international reference networks,

including CORSs, the International GNSS Service

(IGS), and satellite-based augmentation system (SBAS)

monitoring infrastructures [41], [42], [43], [44].

Although originally designed for precise positioning,

geodesy, or integrity monitoring, these networks

deliver rich signal-level observables that can be

repurposed for interference detection and situational

awareness.

A key limitation of fixed-reference networks is their

spatial distribution, which is often driven by geodetic

or aviation requirements rather than by interference-

monitoring needs. As a result, sensor density may be

uneven, with sparse coverage in remote or maritime

areas. GNSS-SAM architectures must therefore account

for heterogeneous sensor spacing and employ spatial

correlation or interpolation techniques when

visualizing service degradation [42], [45].

Despite these limitations, permanent reference

stations remain a cornerstone of GNSS-SAM due to

their high data quality, operational stability, and

availability of real-time data streams through

standardized protocols such as NTRIP (i.e., Networked

Transport of RTCM via Internet Protocol, where RTCM

means the Radio Technical Commission for Maritime

Services), enabling near real-time situational

awareness [46], [47], [48].

5.2 Mobile Platforms and Opportunistic Sensors

Mobile sensing platforms provide an important

complement to fixed GNSS reference stations in GNSS-

SAM systems. These platforms include land vehicles,

maritime vessels, aircraft, and unmanned aerial

vehicles (UAVs), which are capable of collecting GNSS

measurements while traversing areas that are

insufficiently covered by permanent infrastructure.

Mobile sensors enable dynamic exploration of

interference-affected regions and support spatial

characterization of GNSS disruptions.

In the GNSS-SAM context, mobile platforms are

particularly valuable for mapping the spatial extent of

interference and for assisting in source localization. As

the platform moves through different environments,

variations in signal quality can be observed as a

function of position, providing information that is not

available from stationary sensors. As highlighted in the

presentation, such data can be visualized as trajectories

or velocity vectors overlaid on the situational

awareness map.

However, GNSS data collected from mobile

platforms are inherently more variable than data from

fixed stations. Motion-induced dynamics, changing

satellite visibility, multipath effects, and signal

blockage can introduce fluctuations that are unrelated

to interference. GNSS-SAM systems must therefore

incorporate filtering, normalization, and context-aware

processing to distinguish genuine interference

signatures from mobility-related effects.

In addition to dedicated mobile platforms,

opportunistic sensors embedded in consumer devices,

such as smartphones, represent a growing data source

for GNSS-SAM. Although individual measurements

are less precise, the large number of available devices

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can provide valuable coarse-grained insight into GNSS

performance, particularly in densely populated urban

environments [10].

Such opportunistic sensing approaches enable

large-scale, low-cost monitoring, albeit with lower

measurement reliability and increased uncertainty.

5.3 Specialized Interference Monitoring Sensors

Specialized interference monitoring sensors form the

most advanced data layer within GNSS-SAM

architectures. These sensors are specifically designed

to analyze GNSS signals at a detailed level, often

employing SDR technology to provide access to raw

signal samples in the time and frequency domains.

Such capabilities enable precise characterization of

interference waveforms and spectral signatures.

These sensors can detect a wide range of

interference types, including continuous-wave signals,

pulsed jamming, broadband noise, and spoofing-

related distortions of the correlation function. When

combined with multi-antenna configurations, such as

controlled reception pattern antennas (CRPAs), they

can also support direction-of-arrival estimation, which

is valuable for identifying and localizing interference

sources [49], [50].

Within GNSS-SAM, data from specialized sensors

often serve as a high-confidence reference for

validating events detected by less capable receivers.

Their inclusion significantly improves classification

reliability, particularly in complex spoofing scenarios

where subtle signal anomalies must be distinguished

from benign effects.

Due to their cost and operational complexity, these

sensors are typically deployed at critical locations, such

as airports, seaports, telecommunications hubs, or

military facilities. Integrating their outputs into a

GNSS-SAM framework allows localized high-fidelity

measurements to contribute to a broader, area-wide

situational awareness picture [5].

5.4 Data Integration, Synchronization, and System

Architecture

A central challenge in GNSS-SAM systems is the

integration of data originating from heterogeneous

sensors with differing accuracy, update rates, and

reliability. The system must support both continuous

real-time data streams and asynchronously reported

measurements, while maintaining a coherent

analytical framework. Effective data integration is

therefore a core functional requirement of GNSS-SAM.

Accurate time synchronization is particularly

critical. Misaligned timestamps can lead to incorrect

spatial or temporal correlations, masking genuine

interference events or generating false alarms. GNSS-

SAM architectures typically employ normalization,

buffering, and quality control mechanisms to ensure

temporal consistency across data sources.

From a network perspective, GNSS-SAM can be

implemented using centralized, distributed, or hybrid

architectures. Increasingly, edge computing

approaches are employed to perform preliminary

processing near the data source, reducing

communication latency and bandwidth requirements.

This is especially relevant for mobile or bandwidth-

constrained platforms.

A flexible and modular integration architecture

enables GNSS-SAM systems to evolve over time,

accommodating new sensor types, data formats, and

analytical methods. Such adaptability is essential in the

face of rapidly evolving GNSS interference threats and

monitoring technologies [39], [40], [51]. The overall

conceptual architecture of the proposed GNSS-SAM

framework, including heterogeneous sensor

integration, feature extraction, interference detection,

and operational awareness components, is depicted in

Figure 1.

The integrated and synchronized data streams form

the basis for subsequent feature extraction and

interference detection processes described in Section 6.

Overall, GNSS-SAM data sources and sensor networks

provide a multi-layered observation framework that

enables robust, scalable, and context-aware situational

awareness of GNSS performance.

6 SIGNAL FEATURES AND INTERFERENCE

INDICATORS

6.1 Signal-Level Metrics

Signal-level metrics constitute the primary layer of

information used in GNSS-SAM systems to assess the

quality and integrity of received signals. Among these,

the C/N₀ is one of the most widely used indicators, as

it directly reflects the strength of the received signal

relative to the noise floor. Sudden drops in C/N₀ across

multiple satellites or frequencies often indicate the

presence of jamming or other forms of interference [17],

[18], [52].

In addition to signal strength, Doppler frequency

shift (DFS) measurements provide insight into the

relative motion between satellites and the receiver.

Under nominal conditions, DFSs follow predictable

patterns based on satellite orbits. Deviations from these

expected trends, particularly when observed

simultaneously across multiple satellites, may indicate

spoofing attempts or receiver tracking anomalies [18],

[53], [54].

Pseudo-range residuals, defined as the difference

between measured and predicted pseudo-ranges, are

another important indicator. Elevated residuals may

arise from multipath effects, atmospheric disturbances,

or interference. However, spatially correlated residual

anomalies across multiple receivers are more likely to

indicate deliberate interference. The combined analysis

of these metrics enables robust detection of both

gradual degradations and abrupt disruptions. In

addition, the position dilution of precision (PDOP) can

be used as a geometry-related quality indicator, since

interference-induced loss of satellite tracking may

degrade satellite geometry and increase the expected

uncertainty of the positioning solution [55], [56].

Signal quality monitoring (SQM) techniques

provide an additional layer of signal-level analysis by

evaluating distortions in the correlation function and

tracking behavior of GNSS signals. Originally

developed for safety-critical applications such as

342

satellite-based augmentation systems (SBASs) and

ground-based augmentation systems (GBASs), SQM

methods rely on metrics derived from the shape and

symmetry of the correlation function, including early-

minus-late measurements and slope asymmetry

indicators. These metrics enable the detection of subtle

signal anomalies that may not be observable through

conventional indicators such as C/N₀ or pseudo-range

residuals. In the context of GNSS-SAM, SQM features

enhance the capability to identify spoofing and other

signal deformations at an early stage, particularly

when combined with spatial and multi-receiver

analysis [57], [58], [59].

Figure 1. Conceptual architecture of the GNSS-SAM

framework integrating heterogeneous sensor networks,

feature extraction mechanisms, interference detection

modules, geospatial visualization, and operational

awareness services.

6.2 Correlation Function Distortions

The correlation function represents the fundamental

mechanism by which GNSS receivers acquire and track

satellite signals. Under nominal conditions, the

correlation peak exhibits a well-defined symmetric

shape. However, the presence of interference,

particularly spoofing, can introduce distortions that

alter this shape in detectable ways [60].

Spoofing signals, especially those generated by

sophisticated transmitters, may produce multiple

correlation peaks or asymmetric distortions in the

correlation function. These anomalies arise from the

superposition of authentic and counterfeit signals or

from mismatches in code phase alignment. Monitoring

such distortions provides a powerful means of

detecting spoofing without relying solely on

navigation-domain inconsistencies. Representative

examples of signal degradation effects and

interference-related indicators commonly used in

GNSS-SAM systems are presented in Figure 2.

In GNSS-SAM systems, correlation-based

indicators are often extracted from advanced receivers

or SDR-based sensors capable of accessing raw signal

samples. While this limits their availability in large-

scale deployments, their diagnostic value is significant,

particularly for validating suspected spoofing events

detected using higher-level metrics [60], [61], [62].

Figure 2. Examples of GNSS interference indicators used in

GNSS-SAM systems, including C/N₀ degradation,

correlation function distortion, spoofing-induced position

drift, and satellite visibility reduction.

6.3 Spoofing Detection Indicators

Spoofing detection in GNSS-SAM relies on a

combination of signal-level and navigation-domain

indicators that reveal inconsistencies not expected

under normal operating conditions. One important

class of indicators is related to satellite geometry. For

example, discrepancies between expected and

observed satellite elevation or azimuth angles may

suggest that the received signals do not originate from

genuine satellite positions.

Another important indicator is the presence of time

inconsistencies, such as sudden clock jumps or gradual

time drift affecting multiple satellites simultaneously.

Since GNSS receivers rely on precise timing for

position computation, even small inconsistencies can

be indicative of spoofing attempts. Monitoring these

effects across multiple receivers enhances detection

robustness.

Spatial correlation plays a crucial role in spoofing

detection within GNSS-SAM. If multiple receivers

within a region report similar position shifts or timing

anomalies, this strongly suggests the presence of a

coordinated spoofing attack. Conversely, isolated

anomalies are more likely to be caused by local effects

such as multipath.

Additional indicators include signal consistency

across frequencies and constellations. Spoofing attacks

often target specific signals, resulting in inconsistencies

between different GNSS systems. Multi-constellation

monitoring therefore, significantly improves detection

capability [5], [63].

6.4 Feature Extraction for Classification

Feature extraction is the process of transforming raw

GNSS measurements into a structured set of indicators

suitable for classification and decision-making. In

GNSS-SAM, this involves combining multiple signal-

level metrics, correlation-based indicators, and spatial-

temporal patterns into feature vectors that describe the

state of the GNSS environment.

A key requirement for feature extraction is

robustness against noise and environmental

variability. Features must be designed to minimize

sensitivity to benign effects such as multipath or

atmospheric disturbances while remaining responsive

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to interference signatures. This often involves

normalization, filtering, and statistical aggregation

over time and across sensors.

Another important aspect is dimensionality

management. While a large number of features may

improve detection performance, it also increases

computational complexity and the risk of overfitting in

ML models. GNSS-SAM systems must therefore

balance feature richness with computational efficiency,

particularly in real-time applications.

Finally, feature extraction serves as the interface

between the data acquisition layer (see Section 5) and

the analytical layer (see Section 7). Well-designed

features enable effective application of both rule-based

and ML approaches, forming the foundation for

reliable interference detection and classification [64],

[65]. The general processing workflow employed in

GNSS-SAM systems for signal processing, anomaly

detection, interference classification, and alert

generation is illustrated in Figure 3.

Figure 3. General workflow of GNSS-SAM data processing,

including signal acquisition, preprocessing, feature

extraction, anomaly detection, classification, geospatial

mapping, and alert generation.

7 DATA FUSION AND ANOMALY

CLASSIFICATION IN GNSS-SAM

7.1 Rule-Based Detection Approaches

Rule-based detection remains one of the most widely

used approaches in GNSS interference monitoring

systems due to its simplicity, transparency, and ease of

validation. These methods rely on predefined

thresholds and logical conditions applied to signal

features, such as C/N₀ drops, residual thresholds, or

consistency checks across receivers.

One advantage of rule-based systems is their

interpretability. Each detection decision can be traced

back to specific conditions, which is particularly

important in safety-critical applications such as

aviation. This transparency facilitates certification and

regulatory acceptance, where explainability is a key

requirement.

However, rule-based approaches have limitations

in handling complex or evolving interference

scenarios. Fixed thresholds may not generalize well

across different environments, leading to false alarms

or missed detections. GNSS-SAM systems often

address this by incorporating adaptive thresholds or

combining multiple rules to improve robustness [66],

[67].

7.2 Machine Learning-Based Classification

ML approaches offer a more flexible framework for

GNSS interference classification by learning patterns

directly from data. Supervised learning techniques,

such as support vector machines, random forests, or

neural networks, can be trained to distinguish between

nominal conditions and different types of interference

based on labeled datasets.

These methods are particularly effective in

detecting subtle or previously unseen interference

patterns that may not be captured by rule-based

systems. By leveraging large datasets collected from

GNSS-SAM networks, ML models can improve

detection accuracy and reduce false alarm rates.

Nevertheless, the application of ML in GNSS-SAM

introduces challenges related to training data

availability, model interpretability, and computational

requirements. Ensuring that models generalize across

different environments and remain robust to changing

conditions is a key research challenge [66], [68], [69],

[70].

7.3 Event Characterization and Severity Assessment

Once an interference event has been detected and

classified, GNSS-SAM systems must characterize its

properties to support decision-making. This includes

identifying the type of interference (e.g., jamming,

spoofing), estimating its geographic extent, and

determining the affected GNSS signals or

constellations.

A key aspect of event characterization is

geolocation. By correlating observations from multiple

sensors, GNSS-SAM can estimate the location of the

interference source or define a bounding region where

signal degradation is observed. The accuracy of this

344

process depends on sensor density and measurement

quality.

Severity assessment is typically performed using a

scoring system that quantifies the impact of the

interference on GNSS performance. Metrics such as

signal loss, position error, and affected area can be

combined into a severity index, enabling prioritization

of response actions.

Finally, characterized events can be visualized and

communicated through GNSS-SAM interfaces,

supporting both real-time operational decisions and

long-term analysis. This closes the loop between data

acquisition, analysis, and actionable situational

awareness [34], [35], [71].

8 GEOSPATIAL VISUALIZATION AND GNSS

SAM ARCHITECTURE

8.1 GIS-Based Visualization Framework

Geospatial visualization constitutes a central

component of GNSS-SAM systems, enabling the

transformation of distributed GNSS measurements

into an intuitive operational picture. GIS frameworks

provide the underlying infrastructure for integrating

spatial data layers, including base maps, infrastructure

elements, and GNSS performance indicators. By

leveraging GIS technologies, GNSS-SAM systems can

present heterogeneous data in a unified and

georeferenced form.

A typical GIS-based GNSS-SAM architecture

consists of multiple layers, including a base map (e.g.,

OpenStreetMap [72] or satellite imagery), sensor

locations, and dynamically updated performance

metrics. These layers are continuously refreshed using

real-time data streams and are often combined with

temporal filtering mechanisms to provide a near real-

time view of GNSS conditions. The use of standardized

spatial data formats and web-based GIS services

facilitates interoperability and scalability.

Furthermore, GIS frameworks support advanced

spatial analysis functions, such as interpolation,

clustering, and spatial correlation. These capabilities

are particularly important in GNSS-SAM systems,

where sensor density may vary significantly across

regions. Through spatial modeling techniques, it is

possible to estimate GNSS performance in areas

lacking direct measurements, thereby improving the

completeness of situational awareness [73].

8.2 Performance Heatmaps and Interference Markers

Performance heatmaps are one of the most effective

visualization tools used in GNSS-SAM systems. They

provide a continuous spatial representation of GNSS

signal quality, typically derived from aggregated

metrics such as C/N₀ or positioning error. By mapping

these values onto a grid and applying color-coded

scales, heatmaps allow operators to quickly identify

regions of degraded performance.

As described in the presentation, heatmaps often

employ threshold-based color schemes, where green

indicates nominal operation, yellow represents

moderate degradation, and red highlights severe

interference or signal denial. Such visual encoding

enables rapid interpretation even under time-critical

conditions. Temporal averaging (e.g., over several

minutes) is commonly applied to reduce noise and

highlight persistent patterns.

In addition to continuous heatmaps, discrete

interference markers are used to represent detected

events. These markers are typically categorized by type

(e.g., continuous-wave jamming, broadband

interference, spoofing) and displayed using distinct

symbols or colors. When combined with temporal

information, they provide a clear picture of ongoing

and past interference activity.

The combination of heatmaps and event markers

enhances situational awareness by providing both a

global overview and localized detail. This dual

representation is particularly useful in operational

environments such as aviation and maritime

navigation, where both regional trends and specific

events must be monitored simultaneously [35], [71].

8.3 Velocity and Trajectory Overlays

In addition to static sensor data, GNSS-SAM systems

can incorporate dynamic information from mobile

platforms. Velocity vectors and trajectory overlays

provide valuable insight into the interaction between

moving receivers and interference environments. By

visualizing movement paths alongside signal quality

metrics, operators can better understand how

interference affects navigation in real-world scenarios.

Trajectory overlays are particularly useful for

identifying spatial boundaries of interference zones. As

a mobile platform enters or exits a degraded region,

changes in signal quality can be correlated with

position, enabling the estimation of interference extent.

This is especially relevant for UAV-based monitoring

or maritime applications, where mobility is inherent to

the sensing process.

Velocity vectors further enhance this analysis by

indicating direction and speed of motion. When

combined with temporal data, they allow the

reconstruction of interference events over time,

revealing patterns such as moving jamming sources or

transient disturbances. An example of trajectory-based

visualization and dynamic interference evolution in a

GNSS-SAM environment is presented in Figure 4. This

dynamic visualization capability distinguishes GNSS-

SAM from static monitoring systems.

Figure 4. Example of dynamic GNSS-SAM visualization

integrating trajectory overlays, mobile receiver movement,

temporal interference evolution, and geospatial event

correlation for operational situational awareness.

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Moreover, trajectory-based analysis can support

validation of detected events. Consistent degradation

observed along multiple independent trajectories

increases confidence in the presence of interference,

while isolated anomalies may be attributed to local

environmental effects [10], [74], [75].

8.4 Human-Machine Interface for Decision Support

The human-machine interface (HMI) plays a critical

role in translating GNSS-SAM data into actionable

information. Effective HMI design must balance the

need for comprehensive information with the

requirement for clarity and usability. This is

particularly important in operational contexts where

decisions must be made rapidly.

GNSS-SAM interfaces typically include multiple

visualization components, such as maps, dashboards,

and alert panels. Interactive features, including

zooming, filtering, and time navigation, allow users to

explore data at different levels of detail. Customization

options enable adaptation to specific operational

needs, such as aviation control or maritime monitoring.

A key requirement of HMI design is the

prioritization of information. Critical alerts and severe

interference events must be clearly distinguished from

routine data to prevent information overload. Visual

hierarchy, color coding, and alert escalation

mechanisms are commonly employed to guide user

attention.

Finally, GNSS-SAM interfaces should support

integration with external systems, such as command-

and-control platforms or network management

systems. This ensures that situational awareness

information can be seamlessly incorporated into

broader operational workflows, enhancing overall

system effectiveness [76], [77] .

9 EXISTING GNSS-SAM SOLUTIONS AND

IMPLEMENTATIONS

9.1 Public and Open GNSS Interference Monitoring

Platforms

Several publicly accessible platforms provide insight

into GNSS interference by aggregating data from

distributed sources. These systems often rely on

crowd-sourced data, aviation reports, or open sensor

networks to visualize GNSS disruptions over large

geographic areas. Their accessibility makes them

valuable tools for both researchers and practitioners.

Such platforms include web-based services that

display GNSS jamming intensity derived from aircraft

navigation data or crowd-sourced measurements.

They typically present heatmaps of interference levels

and may incorporate temporal filtering or historical

views. Although their data sources may be

heterogeneous, they provide a useful approximation of

GNSS conditions at a regional or global scale.

One notable advantage of these platforms is their

ability to provide near real-time updates with minimal

infrastructure requirements. However, their reliance

on indirect measurements or non-specialized sensors

may limit accuracy and reliability. Despite these

limitations, they play an important role in raising

awareness of GNSS interference phenomena and their

operational impact [78].

Concrete examples of such platforms are illustrated

in Figures 5 and 6, highlighting differences in data

sources, visualization approaches, and levels of

operational maturity. The GPSJam platform [78], for

instance, presents global maps of GNSS interference

intensity derived primarily from Automatic

Dependent Surveillance–Broadcast (ADS-B) [79], [80]

data collected from commercial aircraft, enabling the

identification of large-scale jamming activity, as shown

in Figure 5(a) [78]. Similarly, spoofing monitoring

services, such as the platform developed by SkAI Data

Services, present suspected spoofing events based on

aviation data analytics [81].

(a)

(b)

(c)

Figure 5. Examples of publicly available GNSS interference

monitoring platforms: (a) GPSJam – global map of GNSS

interference intensity derived primarily from ADS-B data; (b)

GPSwise – real-time visualization of spoofing and jamming

events with trajectory-based analytics; (c) Stanford GNSS

interference monitoring platform – research-oriented system

using ADS-B-derived indicators for global interference

detection.

346

More advanced platforms, such as GPSWise, extend

this approach by providing near real-time monitoring

of both jamming and spoofing events, combined with

trajectory-based visualization and historical analysis

capabilities, thereby supporting operational awareness

in aviation contexts, as illustrated in Figure 5(b) [82]. In

parallel, research-oriented systems such as the

Stanford GNSS interference monitoring platform

(rfi.stanford.edu) employ ADS-B-derived indicators

and signal quality analysis to detect and visualize

interference events on a global scale, as shown in

Figure 5(c) [83].

Additional insight can be obtained from

commercial flight tracking services such as

FlightRadar24, which infer GNSS interference from

degradation of Navigation Integrity Category (NIC)

values embedded in ADS-B messages. This enables the

identification of regions where multiple aircraft

simultaneously experience degraded positioning

performance, as illustrated in Figure 6(a) [84]. A more

detailed spatial representation of interference patterns

can be observed in regional views of GPSJam [78] of

GPSWise maps [81], [82], as shown in Figures 6(b) and

6(c), respectively, highlighting localized disruption

zones.

(a)

(b)

(c)

Figure 6. Additional GNSS interference visualization

platforms: (a) Flightradar24 – GNSS interference inferred

from degradation of Navigation Integrity Category (NIC)

values in ADS-B messages; (b) GPSJam and (c) GPSwise

(regional view) – detailed spatial distribution of interference

levels highlighting localized disruption patterns.

At a regional level, systems such as GNSS-Finland

[12] utilize data from dense CORS networks and

advanced signal monitoring techniques to deliver

more detailed situational awareness within specific

geographic areas. Overall, these examples illustrate the

diversity of current approaches, ranging from crowd-

sourced and aviation-based monitoring to research-

oriented and infrastructure-driven systems. A

comparative overview of selected publicly available

GNSS interference monitoring platforms is

summarized in Table 1.

Table 1. Comparison of selected GNSS interference

monitoring platforms.

9.2 Commercial and Institutional GNSS-SAM Systems

More advanced GNSS-SAM solutions are developed

and operated by governmental agencies, research

institutions, and commercial entities. These systems

typically rely on dedicated sensor networks, including

reference stations and specialized monitoring

receivers, providing higher accuracy and reliability

compared to open platforms.

Institutional systems, such as those developed for

aviation safety, often integrate data from certified

monitoring networks and apply validated detection

algorithms. For example, European initiatives

coordinated by European Union Aviation Safety

Agency focus on monitoring GNSS interference

affecting air traffic operations. Similarly, national

GNSS-SA services provide continuous monitoring

within specific geographic regions.

Commercial systems, on the other hand, may offer

proprietary solutions tailored to specific industries,

such as telecommunications or maritime operations.

These systems often emphasize integration with

existing infrastructure and provide advanced analytics

and alerting capabilities.

Overall, institutional and commercial GNSS-SAM

systems demonstrate the feasibility of large-scale, real-

time monitoring. However, they are often limited by

geographic scope, cost, or lack of interoperability

between different systems [12].

9.3 Limitations of Current Solutions

Despite significant progress, existing GNSS-SAM

solutions exhibit several limitations that hinder their

ability to provide comprehensive situational

awareness. One major challenge is the heterogeneity of

data sources, which complicates integration and

reduces consistency across different platforms.

347

Another limitation is the lack of global coverage.

Many systems are confined to specific regions or rely

on sparse sensor networks, leading to incomplete

visibility of GNSS interference. This is particularly

problematic in maritime and remote areas, where

GNSS is often critical but monitoring infrastructure is

limited.

Additionally, many existing platforms focus

primarily on visualization rather than advanced

analysis. While heatmaps and event markers provide

valuable information, they may not fully capture the

complexity of interference scenarios or support

predictive capabilities. The integration of machine

learning and advanced data fusion techniques remains

an active area of research.

Finally, interoperability and standardization issues

limit the ability to combine data from different

systems. Addressing these challenges requires the

development of unified frameworks, such as the

GNSS-SAM concept proposed in this paper, which aim

to integrate diverse data sources and analytical

methods into a coherent and scalable solution [39].

10 USE CASES AND APPLICATIONS OF GNSS-

SAM

10.1 Aviation and Air Traffic Management

GNSS-SAM systems play a critical role in aviation,

where GNSS-based positioning is widely used for

navigation, landing procedures, and air traffic

management. Recent reports indicate a significant

increase in GNSS jamming and spoofing incidents

affecting aircraft, particularly in regions near

geopolitical conflict zones, posing a direct risk to

aviation safety. As a result, real-time situational

awareness of GNSS performance has become essential

for both pilots and air navigation service providers.

In operational contexts, GNSS-SAM can support air

traffic management by providing a shared, real-time

picture of interference events. Such systems enable

early detection of degraded navigation performance

and allow for timely mitigation actions, such as

switching to alternative navigation systems or

adjusting flight paths. European initiatives emphasize

the importance of combining monitoring and

operational data to create a unified awareness

framework across aviation stakeholders .

Furthermore, GNSS-SAM contributes to post-event

analysis and safety reporting. By correlating data from

multiple aircraft and ground sensors, it is possible to

reconstruct interference events and improve risk

assessment models. This supports regulatory bodies in

developing standardized procedures and mitigation

strategies.

10.2 Maritime and Port Operations

In maritime navigation, GNSS is a fundamental

component of positioning systems used by vessels,

port authorities, and traffic management systems.

However, GNSS signals are particularly vulnerable in

coastal and high-traffic regions, where interference can

disrupt vessel navigation and lead to safety risks.

Recent studies have demonstrated the feasibility of

detecting spoofing events using Automatic

Identification System (AIS) data [85], [86], [87],

highlighting the importance of large-scale monitoring

approaches.

GNSS-SAM systems can enhance maritime

situational awareness by integrating data from vessels,

coastal stations, and satellite observations. By

analyzing spatially correlated anomalies in vessel

trajectories, it is possible to identify potential spoofing

or jamming events affecting multiple ships

simultaneously. This enables port authorities to issue

warnings and implement mitigation measures.

Additionally, GNSS-SAM supports the protection

of critical maritime infrastructure, such as ports and

offshore installations. By providing continuous

monitoring and historical analysis capabilities, these

systems contribute to improved resilience of maritime

operations against GNSS disruptions.

10.3 Telecommunications and Power Grid

Synchronization

GNSS is widely used as a primary source of precise

timing in telecommunications networks and power

grid synchronization systems. Accurate timing is

essential for maintaining network synchronization,

enabling data transmission, and ensuring stable

operation of power systems. Disruptions in GNSS

signals can therefore have cascading effects on critical

infrastructure.

GNSS-SAM systems provide a mechanism for

detecting timing anomalies and identifying potential

interference affecting synchronization services. By

monitoring signal integrity and timing consistency

across distributed sensors, it is possible to detect early

signs of degradation and prevent large-scale failures.

The increasing dependence on GNSS-based timing

has led to growing interest in resilient PNT solutions.

GNSS-SAM can support these efforts by providing

real-time awareness of timing integrity and enabling

the integration of alternative timing sources, such as

terrestrial networks or atomic clocks, into hybrid

architectures.

10.4 Security, Defense, and Border Protection

GNSS interference is often associated with intentional

activities, including electronic warfare, smuggling, and

unauthorized use of jamming devices. As such, GNSS-

SAM systems have important applications in security

and defense domains.

In military contexts, GNSS-SAM can support the

detection and localization of interference sources,

enabling rapid response and mitigation. By combining

data from multiple sensors and platforms, it is possible

to identify patterns of interference and assess potential

threats.

Border protection agencies can also benefit from

GNSS-SAM by monitoring anomalous navigation

behavior in border regions. For example, spoofing may

be used to conceal vessel or vehicle movements, and its

detection can support law enforcement operations.

Finally, GNSS-SAM contributes to national

resilience by providing situational awareness of GNSS

348

threats across critical infrastructure sectors. This

supports coordinated responses to large-scale

interference events and enhances overall security.

11 DISCUSSION AND FUTURE DIRECTIONS

11.1 Scalability and Interoperability

One of the key challenges in GNSS-SAM development

is scalability. As the number of sensors and data

sources increases, the system must efficiently process

large volumes of heterogeneous data in real time. This

requires scalable architectures capable of handling

distributed data streams and performing real-time

analytics.

Interoperability is equally important, as GNSS-

SAM systems must integrate data from diverse

sources, including CORS networks, mobile sensors,

and commercial platforms. The lack of standardized

data formats and interfaces remains a significant

barrier to integration. Addressing this challenge

requires the development of common data models and

protocols.

Future GNSS-SAM systems are likely to adopt

cloud-based and edge computing architectures to

improve scalability and reduce latency. These

approaches enable distributed processing and facilitate

the integration of new data sources.

11.2 Predictive Analytics and Artificial Intelligence (AI)

Driven Threat Assessment

The increasing availability of GNSS monitoring data

creates opportunities for applying advanced analytics

and machine learning techniques. Predictive models

can be used to identify patterns of interference and

anticipate future events, enabling proactive mitigation

strategies.

ML approaches can also improve classification

accuracy by identifying complex patterns that are not

captured by rule-based methods. However, challenges

remain in terms of data quality, model generalization,

and interpretability.

In the context of GNSS-SAM, artificial intelligence

(AI)-driven approaches are expected to play a growing

role in threat assessment and decision support. By

combining historical data with real-time observations,

these systems can provide predictive insights into

GNSS interference trends.

11.3 Integration with Resilient PNT Architectures

The vulnerability of GNSS to interference has led to

increased interest in resilient PNT architectures. These

systems combine GNSS with alternative technologies,

such as terrestrial navigation systems, inertial sensors,

and communication-based positioning.

GNSS-SAM plays a key role in such architectures by

providing real-time awareness of GNSS performance

and enabling dynamic selection of alternative

positioning sources. A conceptual integration of GNSS-

SAM with resilient multi-source PNT architectures is

shown in Figure 7. This enhances system resilience and

reduces dependence on a single technology.

Future research should focus on developing

integrated frameworks that combine GNSS-SAM with

multi-sensor PNT systems. Such approaches will be

essential for ensuring reliable positioning and timing

in the presence of increasing interference threats.

Figure 7. Integration of GNSS-SAM with resilient PNT

architectures combining GNSS monitoring, alternative

positioning technologies, sensor fusion, and adaptive

navigation support mechanisms.

12 CONCLUSIONS

This paper has presented the concept of GNSS-SAM as

a comprehensive framework for monitoring, detecting,

analyzing, and visualizing GNSS interference

phenomena. The proposed approach integrates

heterogeneous data sources, including fixed GNSS

reference stations, mobile sensing platforms, crowd-

sourced observations, and specialized interference

monitoring sensors, into a unified situational

awareness architecture. By combining signal-level

analysis, feature extraction, anomaly classification, and

geospatial visualization, GNSS-SAM enables a

coherent representation of GNSS performance

degradation in both spatial and temporal domains.

The conducted analysis has shown that GNSS-SAM

systems can significantly enhance awareness of

jamming and spoofing activity across multiple

operational sectors. In aviation, such systems support

flight safety and air traffic management by enabling

near real-time identification of navigation disruptions.

In maritime environments, GNSS-SAM contributes to

improved monitoring of vessel navigation and port

operations, while in telecommunications and power

grid infrastructures it supports the protection of time

synchronization services. The presented examples

further demonstrate that GNSS-SAM concepts are

increasingly relevant for security, defense, and critical

infrastructure resilience applications.

The paper has also highlighted the growing

importance of integrating data from distributed and

heterogeneous monitoring infrastructures. Existing

platforms, including public, research-oriented, and

commercial systems, already demonstrate the

feasibility of large-scale GNSS interference monitoring

using data derived from ADS-B observations, CORS

networks, and signal quality indicators. However,

current solutions often remain fragmented,

application-specific, or geographically limited. The

GNSS-SAM concept addresses these limitations by

promoting a scalable and interoperable framework

349

capable of combining diverse sensing modalities and

analytical techniques into a unified operational picture.

Particular attention has been devoted to signal-level

metrics and interference indicators, including C/N₀

degradation, Doppler anomalies, pseudo-range

residuals, SQM, and correlation function distortions.

These features constitute the foundation for both rule-

based and ML-based interference classification

approaches. The analysis indicates that future GNSS-

SAM systems will increasingly rely on data fusion,

predictive analytics, and AI-driven threat assessment

mechanisms to improve detection reliability and

reduce false alarm rates.

Despite significant progress in GNSS interference

monitoring technologies, several challenges remain

open. Scalability, interoperability, real-time

processing, and standardized data exchange

mechanisms continue to represent important research

and engineering issues. Furthermore, the increasing

sophistication of spoofing techniques requires the

development of more advanced detection algorithms

and resilient monitoring architectures.

Future work should therefore focus on the

integration of GNSS-SAM with resilient PNT

frameworks combining GNSS, inertial systems,

terrestrial positioning technologies, and alternative

timing sources. The proposed GNSS-SAM framework

remains conceptual and requires validation using

operational multi-sensor datasets and large-scale

deployments. Future GNSS-SAM architectures should

also consider cyber resilience, secure data exchange

mechanisms, and trustworthiness assessment of

distributed monitoring networks to ensure reliable

situational awareness under contested operational

conditions.

Overall, GNSS-SAM represents a promising

direction for the development of next-generation GNSS

monitoring and resilience systems. By transforming

distributed GNSS observations into actionable

operational awareness, these systems can support both

civilian and governmental stakeholders in mitigating

the impact of intentional and unintentional GNSS

interference. As dependence on satellite-based

positioning, navigation, and timing services continues

to increase, comprehensive situational awareness

solutions such as GNSS-SAM will become an

increasingly important component of resilient PNT

ecosystems.

ABBREVIATIONS

ADS B – Automatic Dependent Surveillance–Broadcast

AI – artificial intelligence

AIS – Automatic Identification System

C/N₀ – carrier-to-noise density ratio

CORS – continuously operating reference station

CRPA – controlled reception pattern antenna

CW – continuous-wave

DFS – Doppler frequency shift

EW – electronic warfare

FGI – Finnish Geospatial Research Institute

GBAS – ground-based augmentation system

GIS – geographic information system

GNSS – global navigation satellite system

GNSS-SA – GNSS situational awareness

GNSS-SAM – GNSS situational awareness map

HAS – High Accuracy Service

HMI – human-machine interface

IGS – International GNSS Service

ISR – intelligence, surveillance, and reconnaissance

M2M – machine-to-machine

ML – machine learning

NAVWAR – navigation warfare

NTRIP – Networked Transport of RTCM via Internet

Protocol

OSNMA – Open Service Navigation Message

Authentication

PDOP – position dilution of precision

PMU – phasor measurement unit

PNT – positioning, navigation, and timing

RF – radio-frequency

RTCM – Radio Technical Commission for Maritime

Services

SBAS – satellite-based augmentation system

SDR – software-defined radio

SQM – signal quality monitoring

UAV – unmanned aerial vehicle

ACKNOWLEDGEMENTS

This work was developed within the framework of the

research grants no. UGB/531-000059-W300-22/2025 on

“Transmission properties of radio wave propagation

environments in military applications” and no. UGB/531-

000128-W300-22/2026 on “Possibility analysis of using

modern technologies in communication systems and

electronic warfare”, sponsored by the Military University of

Technology (WAT), Poland.

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