张林,
董云龙,
关键
海军航空大学信息融合研究所 ??烟台 ??264001
基金项目:国家自然科学基金(61871392, 61531020, 61871391, 61471382, U1633122)，中国博士后科学基金(2017M620862)，“泰山****”建设工程专项经费
详细信息
作者简介:李保珠：男，1989年生，博士生，研究方向为雷达数据处理、航迹关联和误差配准等
张林：男，1986年生，博士，讲师，研究方向为雷达目标检测等
董云龙：男，1974年生，博士，副教授，研究方向为雷达目标组网检测等
关键：男，1968年生，教授，博士生导师，研究方向为雷达目标检测和跟踪与识别、海上目标信息感知与融合
通讯作者:李保珠 libaozhu1324@163.com
中图分类号:TN953计量
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被引次数:0
出版历程
收稿日期:2018-07-17
修回日期:2018-11-06
网络出版日期:2018-11-23
刊出日期:2019-06-01
Anti-bias Track Association Algorithm of Radar and Electronic Support Measurements Based on Track Vectors Hierarchical Clustering
Baozhu LI,,Lin ZHANG,
Yunlong DONG,
Jian GUAN
Institute of Information Fusion, Naval Aviation University, Yantai 264001, China
Funds:The National Natural Science Foundation of China (61871392, 61531020, 61871391, 61471382, U1633122), China Postdoctoral Science Foundation (2017M620862), The Special Funds of Taishan Scholars Construction Engineering
摘要
摘要:针对同平台雷达与电子支援措施(ESM)存在系统误差、上报目标不完全一致等复杂场景下航迹关联鲁棒性和有效性问题，该文提出一种基于航迹矢量分级聚类的雷达与ESM航迹抗差关联算法。首先推导修正极坐标系(MPC)下目标等价测量方程，基于等价测量的近似展开得到目标状态估计分解方程，利用真实状态对消的方法得到航迹矢量，基于高斯随机矢量的统计特性，采用航迹矢量分级聚类的方法提取同源航迹。最后通过实验仿真验证，所提算法在不同系统误差、目标分布密度、检测概率等环境下具有较好的关联效果和鲁棒性。
关键词:雷达/
电子支援措施/
航迹关联/
系统误差/
航迹矢量/
分级聚类
Abstract:To address track-to-track association problem of radar and Electronic Support Measurements (ESM) in the presence of sensor biases and different targets reported by different sensors, an anti-bias track-to-track association algorithm based on track vectors hierarchical clustering is proposed. Firstly, the equivalent measurement is derived in the Modified Polar Coordinates (MPC). Linear relationship between state estimates and real states, sensor biases, measurement errors are established based on the approximate expansion of the equivalent measurement. The track vectors are obtained by the real state cancellation method. The homologous tracks are extracted by the method of track vectors hierarchical clustering, according to the statistical characteristics of Gaussian random vectors. The effectiveness of the proposed algorithm is verified by Monte Carlo simulation experiments in the presence of sensor biases, targets densities and detection probabilities.
Key words:Radar/
Electronic Support Measurements (ESM)/
Track-to-track association/
Sensor bias/
Track vectors/
Hierarchical clustering
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