MLBased Block Sparse Recovery for distributed MIMO Radars in Clutter Environments In distributed MIMO radars, block sparse recovery methods can be used to estimate the multiple targets parameters while the sampling rate at each receiver is reduced by exploiting Compressive Sensing (CS). However, the performances of these methods severely degrade in strong clutter environments. In this paper, we propose a Maximum Likelihood (ML) based block sparse recovery scheme called MLBSR for target localization in these environments. The ML optimization requires a very high computational complexity particularly for the localization of an unknown number of targets. However, the complexity of the proposed scheme is acceptable. The proposed scheme has an accurate performance in clutter environments even with reduced the sampling rate at the receivers.

Compressive SensingBased Bandwidth Stitching for Multichannel Microwave Radars The problem of obtaining high range resolution (HRR) profiles for noncooperative target recognition by coherently combining data from narrowband radars was investigated using sparse reconstruction techniques. If the radars concerned operate within different frequency bands, then this process increases the overall effective bandwidth and consequently enhances resolution. The case of unknown range offsets occurring between the radarsxe2x80x99 range profiles due to incorrect temporal and spatial synchronisation between the radars was considered, and the use of both pruned orthogonal matching pursuit and refined     l 1    norm regularisation solvers was explored to estimate the offsets between the radarsxe2x80x99 channels so as to attain the necessary coherence for combining their data. The proposed techniques were demonstrated and compared using simulated radar data.