Closely Spaced Multi-Target Association and Localization Using BR and AOA Measurements in Distributed MIMO Radar Systems

This work addresses the issue of closely spaced multi-target localization in distributed MIMO radars using bistatic range (BR) and angle of arrival (AOA) measurements. We propose a two-step method, decomposing the problem into measurement association and individual target localization. The measurement association poses a significant challenge, particularly when targets are closely spaced along with the existence of both false alarms and missed alarms. To tackle this challenge, we formulate it as a clustering problem and we propose a novel clustering algorithm. By carefully defining the distance metric and the set of neighboring estimated points (EPs), our method not only produces accurate measurement association, but also provides reliable initial values for the subsequent individual target localization. Single-target localization remains challenging due to the involved nonlinear and nonconvex optimization problems. To address this, we formulate the objective function as a form of the product of certain local functions, and we design a factor graph-based iterative message-passing algorithm. The message-passing algorithm dynamically approximates the complex local functions involved in the problem, delivering excellent performance while maintaining low complexity. Extensive simulation results demonstrate that the proposed method not only achieves highly efficient association but also outperforms state-of-the-art algorithms and exhibits superior consistency with the Cramer-Rao lower bound (CRLB).