A review of localization and sensing technologies for UAV swarms in SAR missions

Unmanned aerial vehicle (UAV) swarms are experiencing a remarkable growth as powerful assets in search and rescue (SAR) missions, offering rapid situational awareness
and wide-area coverage in environments that are often inaccessible or unsafe for human responders. Localisation, the ability of each UAV to accurately determine its position and the relative positions of the other members under extreme and uncertain conditions, stands at the core of swarms’ efficiency. This paper presents a detailed review of localisation and sensing technologies designed or adjusted to support UAV swarms in SAR environments. We follow the literature's fundamental categorisation
into absolute, relative and cooperative localisation frameworks, covering methods that rely on visual, radio and radar sensing, hinting at emerging learning-based
multimodal fusion strategies. Particular emphasis is placed on how these methods address the distinct operational constraints of SAR missions, including (i) unstructured
and highly dynamic environments such as rubble or flooded terrain, (ii) time-critical limitations, (iii) GNSS-denied or degraded conditions, (iv) intermittent communication
links, and (v) critical mission demands. By analysing localisation accuracy and computational trade-offs under such environments, the review highlights integration 
challenges between sensing, communication and coordination layers. Finally, we identify key research gaps-notably the need for lightweight, cooperative localisation
based on confidence and robust multimodal fusion for degraded visual scenes, which have to be addressed to enable resilient UAV swarm deployment in real-world SAR
operations.