Multi-Target Tracking with HRRP-Aided Classification for Space-Based Radar Networks: An Enhanced Hybrid MF-BP Approach

Abstract

Data association for space-based radar multi-target tracking is a significant challenge in dense clutter and complex scenarios, often leading to tracking errors and target loss. This paper proposes a classification-aided message-passing algorithm for space-based radars (CA-MP-SBRs), which employs a hybrid mean-field and belief-propagation (MF-BP) framework enhanced with target classification information. We leverage high-resolution range profile (HRRP) data, which are processed by a convolutional neural network (CNN) to classify targets. The classification output is then seamlessly integrated into the MF-BP framework to jointly infer target kinematic states, visibility states, data association, and class index. Simulation results demonstrate that incorporating HRRP-based classification improves data-association reliability and tracking accuracy. Specifically, compared with Classification-aided Gaussian mixture probability hypothesis density (CA-GM-PHD), the proposed CA-MP-SBRs reduces the average root mean square error (RMSE), optimal subpattern assignment (OSPA), and generalized optimal subpattern assignment (GOSPA) by 33.4%, 31.7%, and 32.1%, respectively, while the corresponding reductions relative to classification-aided labeled multi-Bernoulli tracking for space-based radars (CA-LMB-SBRs) are 23.2%, 25.6%, and 30.3%. These results confirm the effectiveness of CA-MP-SBRs for tracking diverse target types in dense-target scenarios.