Optimizing Compressed Sensing for seeing through walls based on Wireless Signals In this paper, we developed a theoretical and experimental framework for the mapping of obstacles using WiFi, based on a small number of wireless channel samples. This is very challenging due to the numerous channel coefficients to be estimated over the timevarying channel and the channel estimation of a wireless signal transmission to be considered for compressive sampling. In a typical communication system, the signal is sampled at least twice at the highest frequency contained in the signal. However, this limits efficient ways to compress the signal, as it places a huge burden on sampling the entire signal while only a small number of the transform coefficients are needed to represent the signal. To tackle this problem, we will focused on a mathematical optimization problem for the most efficient compressed sensing method called ell_1norm, known as Basis Pursuit. Before optimizing the problem, the noise was removed from the signal, namely, multipath fading. Our experimental results show the improved performance in the number of iterations for obtaining a framework for the mapping of obstacles.

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.