UAV-based dynamic crowd monitoring for rescue prioritization using subspace-driven localization

Rapid and reliable localization of survivors is critical in post-disaster search and rescue operations. However, conventional sensing methods such as visual imaging are often ineffective due to smoke, dust, or debris, and traditional RSSI-based localization suffers from severe multipath interference and noise. This paper proposes a subspace-driven multi-UAV system for dynamic crowd monitoring and rescue prioritization. The system employs Boustrophedon scanning to acquire spatially continuous RSSI sequences from survivors’ Wi-Fi devices. A spatial resampling technique is first applied to regularize the irregular measurements, followed by Hankel matrix construction and Singular Value Decomposition (SVD) to separate signal from noise, yielding robust coarse device locations. A multi-pass scanning strategy tracks devices over time, detects mobility as an indicator of vitality, and maintains device records with expiration mechanisms. Detected devices are mapped to predefined rescue zones, and a weighted priority score is computed for each zone based on device count, mobility, and temporal trends. Simulation results under low and high noise conditions demonstrate that the proposed method significantly outperforms Kalman-filtered trilateration and SVD without resampling. At 6 dB noise, it achieves a 46.7% reduction in RMSE and a 60.0% reduction in outlier ratio. Furthermore, spatial resampling reduces computational time by 17% per device-line, with greater gains expected in high-frequency scenarios. The proposed framework transforms raw RSSI measurements into actionable intelligence for rescue command, enabling efficient resource allocation.