A weighted PSO based randomized step frequency radar with high resolution for compressed sensing

In this paper, a novel randomized step frequency radar with weighted PSO is proposed to recover the range and velocity joint estimating by exploiting sparseness of the targets and invoking compressed sensing (CS) theory. In this algorithm, we abandons the exhaustive list method in the Orthogonal Matching Pursuit (OMP) scheme, which is easy to cause performance degradation in the radar system. Instead, a weighted PSO dynamic optimal method is adopted, where the convergence speed is increased due to the weighted factor introduced in the Particle Swarm Optimization (PSO). The primary advantage of this method lies in being less sensitive to the initial value of target parameters in the case of online optimization process. Moreover, this method overcomes the limitation that the initial parameters must be selected close to the true value of the target, which is not constant in many cases. It is not necessary to know exactly the target parameters when using our approach, instead, coarse coding bounds of target parameters are enough for the algorithm, which can be done once and for all off-line, and it is only necessary to specify the initial scopes of the velocity and the range of the target. Simulation results demonstrate that the proposed weighted PSO approach provides a faster convergence speed and robustness against unpredictable perturbations for range and velocity joint estimating in randomized step frequency radar.