Compressed Sensing-Based Genetic Markov Localization for Mobile Transmitters

With the strengths of quickness, low cost, and adaptability, unmanned aerial vehicle (UAV) communication is widely utilized in next-generation wireless network. However, some risks hidden dangers such as UAV “black flight” disturbances, attacks, spying incidents lead to necessity real-time supervision UAVs. A compressed sensing-based genetic Markov localization method proposed this paper for two-dimensional trajectory tracking mobile transmitter a finite domain, which consists three modules: multi-station sampling module, reconstruction module. In multiple stations are deployed receive signal transmitted by using sensing, motion model constant turn rate acceleration (CTRA) model. we propose direct extract joint cross-spatial spectrum. two-step genetically correct inaccurate points preliminary results generate result. Extensive simulations conducted verify effectiveness method. The show that superior particle filter Monte Carlo at all moments. Specifically, when SNR = 15dB, root-mean-square error (RMSE) 39% 60% lower than other two methods, respectively. Moreover, under premise RMSE result less 30 m, module can reduce running time 33.3%.