SENSOR MODALITIES FOR RELATIVE LOCALIZATION OF COOPERATIVE UAV SWARMS IN GNSS-DENIED ENVIRONMENTS

Abstract

The deployment of cooperative swarms of unmanned aerial vehicles in environments without access to global satellite navigation systems represents one of the key challenges in modern autonomous multi-agent systems. Under such conditions, relative localization among agents must provide sufficient accuracy and consistency while operating under strict constraints on size, weight, and power consumption, which are characteristic of mass-produced micro-scale platforms. This paper presents a systematic review of sensor modalities used for relative localization of cooperative swarms in navigation-denied environments, with a particular focus on their computational properties, observability, and robustness to environmental factors. The study analyzes and classifies three principal classes of sensor modalities: radio-frequency distance measurement based on ultra-wideband communication, passive visual methods including visual–inertial odometry and deep learning–based approaches, and systems employing active optical markers. The analysis demonstrates that radio-frequency ranging methods offer low computational cost but suffer from fundamental observability limitations due to the lack of angular information. Passive visual approaches are capable of achieving high accuracy and global consistency; however, they require substantial computational resources, which restricts their practical applicability on platforms with severe hardware constraints. A critical evaluation indicates that active optical marker systems, when combined with distributed state graph optimization methods, constitute a practically viable compromise between localization accuracy, computational efficiency, and robustness under degraded environmental conditions. Particular attention is given to distributed estimation architectures that enable scalability and consistency without centralized processing. The paper concludes by outlining directions for future research aimed at developing hybrid frameworks capable of dynamically switching between full relative pose estimation and direction-only tracking in challenging operational environments.