Sparsity Aware Fast Block LMS Algorithms for MIMO Radar Imaging

摘要

Multiple-input multiple-output (MIMO) radars exhibit superior performance over traditional single-antenna radars resulting in more accurate estimation of radar cross-section (RCS) coefficients of multiple targets as well as better imaging of the radar scattering scene. In this work, two novel sparsity-aware adaptive estimation schemes, improved proportionate fast block least mean square (IPFBLMS) and improved proportionate fast block least mean square with hard thresholding (IPFBLHT) have been proposed, which achieve faster convergence as well as lesser steady-state excess mean squared error. Simulation results and reconstructed images of the scattering scenes demonstrate the improved performance of the proposed schemes as compared to the existing sparsity-agnostic approaches.