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1 INTRODUCTION
Progressing miniaturization occurring across various
technological domains has enabled the application of
unmanned aerial vehicles (UAVs) in a growing array
of scenarios. Numerous scientific publications and
technical reports have documented the deployment of
UAVs for purposes such as monitoring [1, 2],
conducting search missions [3, 4], or performing
autonomous deliveries [5]. Although individual UAVs,
according to the principle that "two heads are better
than one" has led to the recognition of capabilities
offered by UAV teams, commonly referred to as
swarms. Deploying a UAV swarm in several tasks not
only accelerates their execution but also unveils new
functionalities for UAVs, like forming flying ad-hoc
networks (FANETs). However, these benefits come
with increased system complexity, which directly
impacts financial costs. A fundamental requirement for
swarm functionality is collision prevention among its
members. Methods for collision avoidance can be
classified into two categories: independent,
autonomous collision avoidance, and cooperative
strategies. Figure 1 illustrates this classification. The
independent methods rely on anti-collision systems
installed on each UAV, which do not require
cooperation among swarm participants. These systems
gather information only from their immediate
Overview of Mutual Localization Techniques Between
Unmanned Aerial Vehicles in Swarm
B. Czaja, K. Maślanka, P. Skokowski & J. Kelner
Military University of Technology, Warsaw, Poland
ABSTRACT: The market for unmanned aerial vehicles (UAVs), along with their associated applications and
services, has been developing at a rapid pace in recent years. One of the key emerging trends is the use of UAV
swarms, which enable the execution of complex tasks more efficiently than single platforms. Effective control of
such a swarm, whether by a human operator or autonomously, requires maintaining safe distances between
individual UAVs. This, in turn, necessitates precise navigation and mutual localization within the swarm, posing
both technical and operational challenges. This paper presents a comprehensive review of recent advancements
in relative localization techniques within UAV swarms. With the increasing interest in UAV swarm applications
for tasks such as search and rescue, surveillance, and delivery, accurate and reliable localization methods have
become critical for maintaining formation and avoiding collisions. The paper categorizes localization approaches
into cooperative methods and autonomous sensing and further classifies them by the type of sensor used: optical,
radio frequency, and acoustic. For each category, representative technologies, and algorithms, such as ultra-
wideband (UWB), received signal strength indication (RSSI), angle of arrival (AOA), multidimensional scaling
(MDS), and convolutional neural network (CNN)-based vision systems, are discussed, along with their strengths,
limitations, and suitability for Global Positioning System (GPS)-denied environments. The paper concludes with
an identification of current research gaps, including the challenges of sensor array integration on UAV platforms
and the influence of environmental interference on localization accuracy.
http://www.transnav.eu
the International Journal
on Marine Navigation
and Safety of Sea Transportation
Volume 19
Number 1
March 2025
DOI: 10.12716/1001.19 01.37
318
surroundings via sensors such as SONAR, RADAR,
LiDAR, cameras, or light detectors [7]. The main
advantage of such systems is each UAV's full
autonomy within the swarm, enabling them to avoid
both inter-UAV collisions and those with
environmental obstacles.
Figure 1. UAV Swarm solutions to collision mitigation.
Cooperative location techniques rely on the
collaboration among swarm members to achieve
comprehensive situational awareness regarding their
relative positions. This method introduces increased
system complexity, primarily due to the necessity of
maintaining communication among individuals and
establishing communication protocols.
The task of locating UAVs within a swarm can be
accomplished in many ways. Each approach involves
measuring a physical quantity and then estimating the
UAV's position in the swarm based on the measured
values. Measurement techniques can be divided
according to the method of collecting measurements
(antenna arrays, microphone arrays, UWB (Ultra
WideBand) technology, vision systems, laser systems,
and satellite navigation. Various technologies facilitate
these methods, including Bluetooth Low-Energy
(BLE), WiFi, UWB, RFID, RGB cameras, UV cameras,
IR cameras, depth cameras, LIDAR, lasers, and GNSS.
The algorithms responsible for determining the
positions of UAVs within the swarm are directly
related to the measured quantity. Some algorithms rely
on the distances between the UAVs (RSS, TOA, TDOA)
and others on the signals' angle of arrival, including
AOA and vision systems.
This paper outlines various solutions for mutual
location of UAVs within a swarm. The content is
organized as follows: Section II gives a summary of
solutions based on measurement methods. Section III
discusses several algorithms aimed at estimating the
positions of UAVs in the swarm, and Section IV
includes a summary along with unresolved research
questions.
2 MEASUREMENT METHODS
Each system that interacts with the physical world
includes a measurement system tailored to a specific
task. This section discusses the measurement systems
employed by drone swarms to execute the task of
mutual localization and presents their costs and
benefits in table 1. Most of the sensors proposed in the
literature on swarm localization can be classified into
three categories: optical, radio, and acoustic sensors [8,
9].
2.1 Optical sensors
Within the category of optical sensors, we distinguish
cameras, radiation detectors operating in the visible,
infrared (IR), and ultraviolet (UV) spectra. The
application of laser technologies is infrequent. In the
academic literature, the process of identifying an
object's spatial position from a camera image is referred
to as Pose Estimation. By understanding the geometric
parameters of a landmark and the camera's projection
matrix, it is possible to ascertain the landmark's
position relative to the optical system. Figure 2
illustrates this operational principle.
Figure 2. Principle of pose estimation
In their work, Saska et al. [10, 11] deeply explored
the UVDAR system, which utilizes cameras and LED-
based landmarks operating within the ultraviolet
spectrum. Each UAV in the swarm has a set of cameras
and LEDs oriented differently. The researchers exploit
the reduced detail in UV camera images to employ
computationally efficient algorithms for detecting and
estimating marker positions. In this system, each UAV
in the swarm possesses a unique identification number
embedded in the pulses that control the LED markers.
The study detailed in [12] discusses the application of
the UVDAR system for swarm flocking, employing a
distributed control mechanism and radio
communication between members.
Commonly available cameras that operate in visible
light have become the basis for many systems
incorporating optical sensors. Reference [13]
introduces a stabilization system for UAVs leveraging
RGB cameras and circular markers. Meanwhile, the
study in [14] examines a scenario where a lone UAV,
lacking access to satellite location services, is assisted
by a supporting swarm. The UAV determines its
position relative to the swarm using camera imagery
and the swarm members' coordinates. The data is
processed using the PDA (Probabilistic Data
Association) algorithm. A similar problem was
considered in [15], where a swarm of two UAVs fitted
with GNSS receivers aids a UAV with a nonfunctional
receiver. The UAV without satellite data relies on the
swarm members' location information and estimates
its position utilizing camera images through a
Convolutional Neural Network (CNN). The paper [16]
focuses on the use of edge computing and AI for
mutual localization, presenting a vision system for
determining UAV position from camera images. An
edge computer processes the visuals to identify the
UAV and its rotors, which serve as markers, with
position estimates derived using the YOLOv8-pose
model. In [17], a system is proposed for spatial swarm
localization through exchanging environmental
information obtained from camera imagery. Every
UAV is equipped with a camera, a space orientation
measurement capability, and a system for gauging
distances from other swarm members. The camera
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images are processed to extract landmarks. The
authors conducted simulation experiments to prove
the possibility of accurate UAV swarm localization by
sharing environmental features.
Methods for locating markers in camera images can
be affected by partial or full obstruction. In response,
the authors [18] introduced an approach involving a
circular marker outfitted with multiple infrared (IR)
LEDs. The system is based on a camera operating in the
visible band with an IR filter. Additionally, a method
of measuring the distance between the camera and
marker utilizing pixel brightness levels of the marker.
This solution was evaluated indoors against the
Motion Capture system.
Du et al. [19] proposed innovative method for
mutual localization of UAVs within a swarm that relies
on Resonant-Beam technology and laser rangefinders.
This technology involves laser communication by
creating a connection between two modules, where
photon amplification is maintained by constant photon
supply. Each module consistently transmits laser
beams and reflects incoming beams using highly
reflective reflectors. These technologies permit the
calculation of angles and distances between two UAVs.
The authors performed simulation studies involving a
scenario where a swarm is comprised of two UAVs.
2.2 Radio frequency sensors
For sensors utilizing radio technologies, there is a
noticeable increase in both the diversity of
measurement methods and the types of quantities
measured when contrasted with optical methods. Fian
and team conducted simulation studies on systems
that use angle of arrival (AoA) measurements on
antenna arrays, as illustrated in Figure 3. They
suggested equipping each member of the swarm with
an antenna array. Papers [20, 21] detail a technique for
determining the position of UAVs within a swarm by
treating the swarm as a collective virtual antenna array.
By measuring the angles at which signals hit the
individual arrays on the UAVs, it has been
demonstrated that it is possible to estimate the
distances between swarm members. This distance
information is encapsulated in the control matrix a(θ)
corresponding to the array formed by all UAVs. The
simulation outcomes of least squares (LS) estimation
are provided.
Figure 3. Principle of angle of arrival (AoA) estimation
The most widely used method for measuring
distance in UAV swarms relies on RSSI (Received
Signal Strength Indicator) or UWB (Ultra Wide-Band)
technology. As described by the authors in [22, 23],
there are systems designed to estimate UAV distances
using RSSI. This approach employs a radio
propagation model, where the equations factor in both
the RSSI and the distance between antennas placed on
various UAVs. By transmitting a radio signal, the
distance can be assessed through the RSSI
measurement. With UWB technology, distance
measurement occurs by tracking the time of flight for
very short-duration radio pulses. Illustrated in Fig. 4,
the TWR (two-way ranging) technique assesses the
signal's travel time in both directions, enabling an
estimation of the distance between two radio nodes.
Figure 4. Principle of TWR method
In studies [24, 25], systems for the mutual
localization of UAVs in a swarm with limited satellite
navigation were introduced. These studies assumed
that only a subset of the swarm participants were
equipped with GNSS receivers. Using UWB
transceivers they broadcast their geographic
coordinates, allowing those without satellite
navigation to determine distances between themselves.
In [24], an extended Kalman filter (EKF) was employed
to estimate the positions of individual UAVs within the
swarm. The authors in [25] addressed the problem of
locating a swarm of UAVs conducting a mission over a
marine area without satellite navigation access.
The findings from a study on the mutual
localization system in a group of 7 UAVs were detailed
in [26]. To measure the distance between swarm
members, UWB modules were employed. Distance
measurement anomalies were filtered through data
fusion with the Inertial Measurement Unit (IMU) using
an Extended Kalman Filter (EKF). Conversely, in a
related study, the authors in [27] opted for the Sigma
Point filter (also known as the Unscented Kalman
Filter, or UKF), which effectively handles the EKF
problem with strong nonlinearity of the modeled
system.
The work presented in paper [28] explores a system
that employs UWB technology for distance
measurement; however, the authors approached the
localization issue by splitting it into two distinct
phases. In the first stage, they focused on choosing a
single stationary UAV to act as a beacon, serving as a
320
reference point for the swarm's other members to
determine their locations. In the second phase, the
UAVs are mobile and determine their mutual positions
through estimates generated by EKF.
2.3 Acoustic sensors
For acoustic sensors, the medium for sound waves
imposes restrictions on the selection of measurable
quantities. Ultrasound can be used for distance
measurement, but it only works over short distances,
necessitating a densely packed UAV swarm.
Alternatively, microphone arrays can determine the
angle of arrival of a signal originating from another
UAV, as discussed in the studies [29, 30].
[29] discusses the research concerning the
application of various algorithms for estimating the
angle of arrival (AoA) and the effect of rotor angular
speed (regarding noise generated by rotors) on the
accuracy of AoA estimation for chirp signals.
In another study, [30] explores a UAV swarm
localization system employing microphone arrays. The
proposed method involves a single UAV circulating
around a stationary reference point, functioning as a
beacon by emitting an acoustic signal that helps locate
the rest of the swarm members.
Table 1. Advantages and limitations of measurement
methods
Method
Advantages
Limitations
Optical
sensors
-Simple and robust
algorithms for pose
estimation
-Simultaneous
localization of
swarming UAVs and
collision avoidance
with the environment
-High availability of
wide variety of
sensors
-Image signal
processing and AI
demand high
computational power.
- Highly sensitive to
NLOS (Non Line of
Sight)
-High noise and
detection difficulty for
RGB cameras
-Small FOV (Field of
View ) for traditional
cameras
-Average precision of
location
RF
sensors
-No special markers
required (uses RF
transmitters and
receivers)
-Ability to measure
range between UAVs
and AoA
-Efficient algorithms
for AoA and range
estimation
-Can achieve high
precision of location
-Possible sensor
fusion (AoA + range
measurement)
-UWB based sensors
suffer from outliers in
measurements
-AoA measurement
requires costly
antenna array system
to be placed on every
swarm participant
-The RF transmitters
must strictly comply
to the current
regulations
Acoustic
sensors
-No special markers
required (can use
speakers or UAV
rotor noise)
-Efficient algorithms
for AoA estimation
-Can achieve high
precision of location
-Small range of
acoustic waves (in
comparison to RF and
light)
-In noisy
environments the
sensor can be easily
overwhelmed (e.g.
construction site)
-No range based
sensors
3 POSITION ESTIMATION
The prior section described procedures for measuring
physical quantities to estimate UAV positions within
the swarm. This section offers a summary of various
localization methods based on the distance
measurements (Fig. 4) and angles of arrival (Fig. 5).
A widely adopted approach for addressing the
issue of mutual localization is the MDS-MAP algorithm
[31], which leverages the MDS (Multi-Dimensional
Scaling) technique. MDS assists in dimensionality
reduction of statistical data while retaining a chosen
distance metric, such as the Euclidean norm or angular
distance. In the MDS-MAP algorithm, each network
member develops local maps indicating node
positions. These local maps are then integrated to
produce the final node location estimate for the
network. However, MDS-MAP is burdened by high
computational complexity (2*O(n^3)), substantial
localization errors in sparsely connected networks, and
vulnerability to noise in distance measurements.
Figure 5. Position estimation based on range measurements
Figure 6. Position estimation based on angle measurements
Reference [32] describes an alternative approach
utilizing the MDS algorithm. Furthermore, to achieve a
solution approximating the optimum, an SDP
(Semidefinite Programming) optimization technique
has been suggested. The resulting SDP-GM algorithm
iteratively refines the estimates of internodal distances
in accordance with the measured distance values. Each
node uses the MDS method to ascertain the local
network configuration and then adjusts it through
broadcasting its results and acquiring the outcomes
from other nodes. The algorithm terminates when any
of the nodes receives a comprehensive map
encompassing all network members. The SDP-GM
enhances localization accuracy compared to MDS-
MAP, though it comes with higher computational
demands. Additionally, this method is not resilient to
node losses or the incorporation of new nodes.
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The authors of [33] introduced the PACNav
(Persistence Administered Collective Navigation)
algorithm designed for distributed and cooperative
navigation within a swarm of UAVs. In this swarm, the
UAVs are split into two categories: informed members,
who possess a destination and can plan a route to it,
and uninformed members, who lack such information
and rely on observing nearby UAVs through an
omnidirectional camera to navigate.
The study [34] introduces a technique for
determining the location of a UAV swarm inside a
building using UWB technology. The swarm is divided
into clusters, each led by a master node capable of
inter-cluster communication, while the remaining
UAVs can only communicate within their cluster. The
algorithms FSICL (Firefly Swarm Intelligence
Cooperative Localization) and FSACL (Firefly Swarm
Intelligence Automatic Clustering) are introduced,
both derived from the Firefly Swarm optimization
algorithm. FSICL integrates Chan's algorithm[35],
which resolves hyperbolic location equations, with the
firefly swarm approach. FSACL functions as a
Clustering Engine to reduce computation for swarms
with numerous members. Within each cluster, UAVs
identify their positions relative to one another. For a
comprehensive view of the swarm's arrangement,
clusters determine their positions by measuring
distances between master nodes and disseminate their
local configurations. Another study [36] also suggested
cluster division within the swarm and developed an
algorithm using the MDS-MAP method. The authors
recommend framing the issue of mutual localization in
a UAV swarm as a Coalition Formation Game within a
multi-agent system. This approach is intended to
facilitate the analysis of the problem and enable the
adaptation of game theory algorithms to solve the
localization problem.
Reference [37] introduces an enhanced algorithm to
the MDS method, termed SMDS(P)-Ny-PM (Super
Multidimensional Scaling with Nyström
approximation and the Power Method). This algorithm
aims to enhance computational efficiency by
employing the Nyström approximation for the location
estimation error covariance matrix. Additionally, the
power method is utilized to the process of computing
the eigenvalue decomposition (EVD) of the
approximated covariance matrix distribution, thereby
reducing computational complexity.
In [38], the DTN (Dynamic Triangulation) method
is utilized for determining the location of nodes within
a FANET network through the AODV (Ad-hoc On-
Demand Distance Vector) routing protocol. This
algorithm relies on the distance measurements
between the master node and other sub-nodes, derived
from the RSSI (Received Signal Strength Indicator)
levels. The master node's location signal is captured by
the slave nodes, which each then broadcast their
measured RSSI. Among them, the three nodes with the
strongest signal and unknown positions are selected.
The location is then estimated based on their distance
measurements by minimizing the quality measure of
the square of estimation errors.
Authors in reference [39] suggested deploying a
hovering UAV swarm to provide location services in
regions lacking a GNSS signal. The role of each UAV
within the swarm is to ensure a compact and stationary
arrangement. This stability is crucial for the user
localization method, which relies on measuring the
distance between the user and at least 4 stationary
anchors, namely UAVs. The localization problem is
framed as a non-convex optimization task, for which
the Differential Evolution (DE) method has been
proposed.
The problem of mutual location of UAVs within a
swarm is integral to the process of arranging a
formation of flying drones. This scenario is elaborated
in paper [40], where the authors suggest employing an
optimization algorithm to form the intended swarm
configuration. A key aspect of their method involves
an algorithm for determining the relative positions
within the swarm using eigenvalue decomposition
(EVD) of the covariance matrix of location errors.
4 CONCLUSIONS
This article provides an overview of the recent
advancements in the field of mutual location within
UAV swarms. We have categorized the discussed
papers into those discussing methods of measuring
physical quantities and presenting localization
algorithms based on measurements. For measurement
methods, we further classified them according to the
sensor types utilized. Optical methods relied on
applications of cameras and laser systems; radio
sensors employed receivers operating in different
ranges of the radio spectrum, and acoustic sensors
used microphone arrays.
Optical methods together with radio approaches
utilizing UWB technology are highly favored among
researchers. Within these methods, the most significant
volume of research has been focused on addressing the
issue of mutual localization of UAVs within a swarm.
The literature seldom addresses radio and acoustic
techniques that measure the angle of arrival, marking
these methods as a niche research area. While sound
waves have seen various theoretical studies and
practical hardware implementations, radio waves in
angular approaches remain largely untested in actual
environments, with only limited confirmation from
simulation studies. Both types of methods involve
sensor arrays, presenting unique challenges, especially
when deployed on unmanned aerial platforms.
One initial issue to address concerning angular
methods is choosing the array layout to achieve
optimal directional parameters while maintaining a
compact aperture. Additionally, the maximum swarm
density (smallest distances between UAVs) required
for efficient mutual positioning is another factor to
examine. Furthermore, we think the influence of
acoustic and electromagnetic interference on the
accuracy of AoA estimation in both system types
should be reviewed.
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