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The problem of transmit code design in multiple-input multiple-output (MIMO) radar systems is addressed in this paper. The problem is considered in both colocated and widely separated antenna radars in the presence of signal-dependent interference and target mobility. Due to complexity of exact expressions of the optimal detector in the MIMO radars, information theoretic criteria are employed as design metrics which results in a general form of nonconvex optimization problems. In order to tackle the problem, a novel technique based on the minorization-maximization algorithm is proposed and solutions are presented under practical constraints on the transmit code, namely energy constraint, peak-to-average-power ratio constraint, and similarity constraint. Furthermore, the devised method is extended to be robust against uncertainties of the clutter and interference statistics. Finally, numerical examples are used to show the performance of the proposed technique in different situations. [ABSTRACT FROM PUBLISHER]

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]