Super-resolution Localization of Moving Target Based on Improved Compressive Sensing Algorithm

Aiming at the localization problem of moving targets in two-dimensional planar scenes, this paper proposes an improved compressive sensing algorithm for the super-resolution localization of moving targets. Firstly, a multi-target location model is presented in a moving scene according to the detection principle of frequency-modulated continuous wave radar (FMCW) and the fast Fourier transform (FFT) method. Then, considering the data characteristics of the IF signal group, the multiple target location problem is transformed into optimization problems such as sparse signal recovery and reconstruction by adopting compressive sensing theory (CS). A free-grid compressive sensing algorithm is also designed to avoid grid mismatch and improve positioning accuracy. On this basis, the particle swarm optimization (PSO) algorithm is applied to optimize the target’s direction of arrival information (DOA), further reducing the algorithm’s complexity and ensuring the solution’s existence and optimization. Finally, the numerical simulation results show that the proposed algorithm has high positioning accuracy and good real-time performance, which can achieve cooperative super-resolution positioning for multiple moving targets.