@article{Czaja_Maslanka_Skokowski_Kelner_2025,
	author = {Czaja, Bartosz and Maslanka, Krzysztof and Skokowski, Pawel and Kelner, Jan},
	title = {Overview of Mutual Localization Techniques Between Unmanned Aerial Vehicles in Swarm},
	journal = {TransNav, the International Journal on Marine Navigation and Safety of Sea Transportation},
	volume = {19},
	number = {1},
	pages = {317-323},
	year = {2025},
	url = {./Article_Overview_of_Mutual_Localization_Czaja,73,1507.html},
	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.},
	doi = {10.12716/1001.19.01.37},
	issn = {2083-6473},
	publisher = {Gdynia Maritime University, Faculty of Navigation},
	keywords = {Collision Avoidance, Unmanned Aerial Vehicle (UAV), Artificial Intelligence (AI), Navigation systems, Sensor Technology, Swarm, Localization, Communication Systems}
}