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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]

Sparse linear arrays (SLAs) provide a high number of degrees of freedom (DOF) and reduce the mutual coupling effect between sensors of the array. Many design methods for SLAs have been proposed in the last two decades but most of the existing SLA design methods fail to achieve minimum number of sensors for a desired DOF. In this paper, a design method is proposed which utilizes the branch and bound (B & B) optimization algorithm to give a SLA with the desired DOF and minimum number of physical sensors. The proposed SLA design method is obtained by generalizing the minimum sensor array (MSA) design method which is recently proposed in the literature. This generalized method provides the same DOF as the MSA method but with a smaller number of physical sensors. In addition, a version of this design method is proposed which provides more robustness against mutual coupling effect between the sensors of the array. Simulation results demonstrate the superiority of the performance of the SLAs obtained with the proposed method over the arrays obtained with the MSA method and also over the other existing SLAs. [ABSTRACT FROM AUTHOR]