一种多站雷达协同时空误差配准方法研究OA北大核心CSTPCD

In a multi-radar collaborative system,each node operates asynchronously,and measurement errors from multiple radar nodes on the same or multiple targets are inconsistent.This inconsistency leads to misidentifying the same target as different ones during data fusion in a multi-radar collaboration,thereby degrading the performance of the fused track and losing the advantages of multi-radar collaboration.A track error calibration method based on multi-station collaborative data processing is investigated in this paper.It uses least squares filtering to associate multi-station radar tracks while leveraging the advantages of multi-station col-laborative association to eliminate retransmission interference tracks and achieve temporal unification of tracks detected for the same target by multiple stations.Additionally,the iterative closest point(ICP)algorithm based on multi-station collaboration is em-ployed to align low-precision node data with high-precision node data within the multi-station collaborative system.Simulation re-sults demonstrate that the proposed method achieves a higher track association accuracy rate in complex multi-target scenarios and possesses strong resistance to retransmission interference.Compared to traditional spatial error alignment algorithms,it exhibits a lower root mean square alignment error.