多站雷达协同系统各个节点工作具有异步性,多节点雷达对同一个或多个目标的测量误差不一致,导致在多站雷达协同进行数据融合时将同一目标误认为不同目标,使得融合航迹性能下降,失去多雷达协同的优势。文中研究了一种基于多站协同数据处理的航迹误差校准方法,利用最小二乘滤波对多站雷达航迹进行关联,同时利用多站协同关联的优势剔除转发式干扰航迹,对多站探测到的同一目标航迹进行时间统一。并基于多站协同迭代最近点算法,使用多站协同系统中高精度节点数据对低精度节点数据进行误差配准。仿真结果表明,文中方法在多目标复杂运动场景中具有更高的航迹关联正确率,且具有较好的抗转发干扰能力。与传统空间误差配准算法相比,具有更低的配准均方根误差。

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 collaborative 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 employed to align low-precision node data with high-precision node data within the multi-station collaborative system. Simulation results 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.

TN974