Adaptive compressed sensing based randomized step frequency radar with a weighted PSO

In this paper, a novel randomized step frequency radar that combined the adaptive waveform design and the off-grid point effect simultaneously in the scheme of weighted Particle Swarm Optimization(PSO) is proposed, and the range and velocity joint estimating are recovered by exploiting sparseness of the targets and by invoking compressed sensing (CS) theory. In this new mechanism, each of the dictionary matrix element was first extended by adopting Taylor expansion to an arbitrary precise off-grid point, instead of only the points in a discrete form. Then by adding the new generated information into the dictionary matrix adaptively, an updated time-varying new dictionary matrix is yielded. Finally, in order to overcome the local minima in the traditional CS theory, a weighted PSO dynamic optimal method is adopted, where the convergence speed is increased due to the weighted factor introduced in the PSO. 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. The proposed weighted PSO based waveform design approach has the potential to achieve much higher estimation accuracy, a faster convergence speed and robustness against unpredictable perturbations for range, a high precision in randomized step frequency radar.