Deep Learning-Assisted UAV Localization Framework for Post-Disaster Search and Rescue Missions

Abstract

The precise locating of trapped victims is arguably the most challenging issue in SAR operations, particularly when infrastructure is destroyed and SAR teams only have low-power beacon signals from smartphones to search with. This paper presents a framework for centralized and cooperative UAV-based localization with deep learning-based channel classification and 3D environment-adaptive target estimation. A CNN-LSTM classifier is employed based on a dataset generated at 867.5 MHz with NYUSIM. This classifier labels the individual links of a UAV to a target. Then, these labels activate specific localization solvers: a first-order Taylor-expanded WLS method for LOS settings, an LSRE-SOCP method with iterative refinement for NLOS, and a hybrid projection-based scheme for mixed environments. Through exhaustive simulations, this paper shows that the proposed framework significantly reduces runtime and yields high localization accuracy even when the transmit power and path-loss conditions are unknown. The method can be used in diverse propagation scenarios, making it easy to deploy in reality. The whole NYUSIM-generated dataset is made publicly available to support future research on disaster-aware wireless localization.