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.
In this paper, we focus on the theoretical localization accuracy of two localization algorithms in noncoherent MIMO radar systems with widely separated antennas. The first one is the optimal method for multitarget localization which is simply to expand the dimension of the parameter vector and thus perform a global maximum of the joint likelihood function of all the targets. The second one is a suboptimal called successive-interference-cancellation (SIC) algorithm proposed in our previous work [1] which localizes targets one-by-one and clears the interference of previous declared targets. The Cramer-Rao lower bound (CRLB) for these two algorithms has been derived and compared with emphasis on special cases where some targets share no common range bins with any other targets. Numerical results demonstrate that the suboptimal SIC algorithm has little theoretical performance loss compared with the optimal method even when targets share some common range bins and the loss may be reduced by increasing the number of radar elements.
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