基于特征辅助的滑窗式全局最优关联算法

Feature-Aided Global Optimal Data Association Algorithm Based on Sliding Window

针对卫星电子信息的数据特点与传统关联算法中存在的误关联、杂波干扰大等现象,提出一种基于特征参数辅助的滑窗式全局最优辐射源关联算法。该算法利用运动与特征参数信息综合计算观测与目标间的关联度,构建关联模型,求解全局最优的航迹关联关系,并采用滑窗处理技术对航迹进行连续监测与维持,对错漏航迹进行修正。仿真实验表明,算法可满足多源卫星电子对近距离交叉目标、存在非共同观测目标和杂波密集环境下多目标的有效跟踪,且性能优于传统关联算法。

Aiming at the data characteristics of satellite electronic information and the erroneous correlation and clutter interference existing in traditional correlation algorithms,a feature-aided global optimal data association algorithm based on sliding window is proposed.The algorithm uses motion and feature parameter information to comprehensively calculate the correlation between observations and targets.A correlation model is built and the global optimal relationship between tracks is solved.The sliding window processing technology is used to continuously monitor and maintain the track and correct the wrong and missed tracks.Simulation experiments show that the proposed algorithm can satisfy the effective tracking of multi-source satellite electronics in close-range crossing targets and the presence of non-common observation targets and clutter dense environments.Its performance is better than the traditional correlation algorithms.