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Multiple-input multiple-output (MIMO) radars offer higher resolution, better target detection, and more accurate target parameter estimation. Due to the sparsity of the targets in space-velocity domain, we can exploit Compressive Sensing (CS) to improve the performance of MIMO radars when the sampling rate is much less than the Nyquist rate. In distributed MIMO radars, block CS methods can be used instead of classical CS ones for more performance improvement, because the received signal in this group of MIMO radars is a block sparse signal in a basis. In this paper, two new methods are proposed to improve the performance of the block CS-based distributed MIMO radars. The first one is a new method for optimal energy allocation to the transmitters, and the other one is a new method for optimal design of the measurement matrix. These methods are based on minimizing an upper bound of the sum of the block-coherences of the sensing matrix blocks. Simulation results show an increase in the accuracy of multiple targets parameters estimation for both proposed methods. [ABSTRACT FROM AUTHOR]

Multiple pulse repetition frequency (PRF) radars are used for increasing the maximum unambiguous range and Doppler frequency. Because of the nonuniform transmitted pulses, special techniques are required for spectrum analysis of the received signal in these radars. An algorithm for spectrum analysis of multiple PRF radars is presented here. The algorithm is based on the compressive sensing (CS) theory and uses the sparse characteristic of received signal in frequency domain and universal sampling pattern of multiple PRF pulses for determining the Doppler frequencies of targets. Moreover, a CS-based algorithm for blind estimation of the number of targets is presented. The proposed algorithms work properly in low sampling rates and in the presence of multiple targets and Gaussian clutter. Simulation results are presented for illustrating high performance of the algorithms. [ABSTRACT FROM AUTHOR]