  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 the minimization of an upper bound of the sensing
matrix block-coherence. Simulation results show an increase in the accuracy of
multiple targets parameters estimation for both proposed methods.
