Topology-Feature-Based Joint Association and Registration Method in Multiradar System for Group Target Reconstruction

Group targets such as birds and UAVs are the research hotspot in the radar field recently. Group target reconstruction that estimates the positions of individual targets becomes one of the core research requirements. But group targets are usually closely spaced with similar velocity and it is hard to distinguish multiple targets, resulting in angular glint. Meanwhile, they usually have weak echo and fly in a low altitude, which leads to lots of missed detection and false alarms. These characters increase the difficulty of group target reconstruction. Compared with a single radar, multiradar system with complementary multi-view observation can improve the accuracy and completeness of reconstruction. However, a large number of missed detection and false alarms make it difficult to correctly associate multisensor measurements. Moreover, large sensor bias further aggravate the difficulty. This paper proposed a topology feature aided joint association and registration algorithm to simultaneously acquire association and bias estimation results. The topology feature is applied in the association step to improve association correctness. A nonlinear least median of squares estimator is proposed in the registration step to improve the accuracy and robustness of bias estimation. Multi-frame information is used to further improve the association and registration performance. Simulation and experiment results show the better bias estimation and association performance of the proposed method compared with existing algorithms, and better reconstruction performance compared with a single radar.