Joint multi-target detection and localization with a noncoherent statistical MIMO radar

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 of targets. Besides, without the prior information of target number, a multi-hypothesis test strategy on target number 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 but robust suboptimum 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 test detector is no longer required when target number is unknown. Simulation results show the proposed suboptimum algorithms can correctly detecting and accurately localizing multiple targets even when targets share common range bin in some paths.