A Clustering-Based Measurement Association Method for 3-D Dense Multi-Target Localization in Distributed MIMO Radar Systems

To address the difficult problem of measurement association for three-dimensional dense multi-target localization in distributed MIMO radar systems, a novel measurement association method using bistatic range (BR) and angle of arrival (AOA) measurements is proposed in this paper. First, candidate target point sets are generated by utilizing the BR and AOA measurements from each channel. Subsequently, an improved DBSCAN-based clustering algorithm that is tailored for distributed MIMO radar systems is employed to cluster the candidate points, thereby achieving measurement association. Simulation results demonstrate that compared with existing measurement association methods, the proposed method does not require prior knowledge of the number of targets and can still exhibit robust measurement association performance under nonideal detection environments such as low signal-to-noise ratio (SNR), small target spacing, and the presence of false alarm and missed detection.