  In this paper, the problems of simultaneously detecting and localizing
multiple targets are considered for noncoherent multiple-input multiple-output
(MIMO) radar with widely separated antennas. By assuming a prior knowledge of
target number, an optimal solution to this problem is presented first. It is
essentially a maximum-likelihood (ML) estimator searching parameters of
interest in a high dimensional space. However, the complexity of this method
increases exponentially with the number G of targets.Besides, without the prior
information of the number of targets, a multi-hypothesis testing strategy to
determine the number of targets is required, which further complicates this
method. Therefore, we split the joint maximization into G disjoint optimization
problems by clearing the interference from previously declared targets. In this
way, we derive two fast and robust suboptimal solutions which allow trading
performance for a much lower implementation complexity which is almost
independent of the number of targets. In addition, the multi-hypothesis testing
is no longer required when target number is unknown. Simulation results show
the proposed algorithms can correctly detect and accurately localize multiple
targets even when targets share common range bins in some paths.
