Semidefinite Programming for MIMO Radar Target Localization using Bistatic Range Measurements

In this paper, we investigates the target localization problem based on bistatic range measurements in multiple input multiple output (MIMO) radar system with widely separated antennas. Under the assumption of uncorrelated Gaussian distributed measurement noises, The maximum likelihood estimator (MLE) is derived for this problem, which is highly nonconvex and difficult to solve. Weighted least squares (WLS) and Semidefinite programming (SDP) are two research directions for solving this problem. However, existing studies can not provide a high-quality solution over a large range of measurements noise. In this work, we propose to add a penalty term to improve the performance of the original SDP method. We further address the issue of robust localization in the case of non-accurate transmitter/receiver position. The corresponding Cramér Rao lower bound (CRLB) is also derived. Simulation results show the superiority of our proposed methods by comparing with other exiting algorithms and CRLB.