基于距离分级聚类的机载雷达航迹抗差关联算法  

Anti-bias Track-to-Track Association Algorithm for Aircraft Platforms Based on Distance Hierarchical Clustering

作　　者：齐林 熊伟 何友 QI Lin;XIONG Wei;HE You(Institute of Information Fusion,Naval Aeronautical University,Yantai,Shandong 264001,China)

机构地区：[1]海军航空大学信息融合研究所,山东烟台264001

出　　处：《电子学报》2018年第6期1475-1481,共7页Acta Electronica Sinica

基　　金：国家重点基础研究发展规划(973计划)项目(No.61331401);国家自然科学基金(No.61471383;No.61531020;No.61471379;No.61102166;No.91538201)

摘　　要：针对目标密集分布、系统误差时变、传感器上报目标不一致等复杂环境下的机载雷达航迹关联问题,本文基于高斯随机矢量统计特性推导出一种基于距离分级聚类的机载雷达航迹抗差关联算法.文中首先推导运动平台等价量测方程,基于等价量测的一阶泰勒级数展开得到全局直角坐标系中状态估计分解方程,基于真实状态对消得到航迹距离矢量并基于距离矢量分级聚类提取同源航迹关联对.文中分别设置了目标密集、随机误差、系统误差适应性实验验证算法性能,仿真结果表明本文算法的关联准确性和环境适应性相比经典的基于参照拓扑特征的航迹关联算法(RET)有较大幅度的提升.To address track-to-track association problem for aircraft platforms in complex condition,where targets are distributed closely,sensor biases are time-varied,and different sensors report different targets,an anti-bias track-to-track association algorithm based on distance hierarchical clustering is proposed according to the statistical characteristics of Gaussian random vectors. Equivalent measurement equation for moving platform is firstly derived,linear relationship between state estimates and real states,sensor biases,measurement errors is established based on Taylor series expansion,distance vector is obtained based on real state cancellation,and homologous tracks are extracted based on distance vectors hierarchical clustering.Adaptability experiments are established based on three factors including different targets densities,random errors and sensor biases. Monte Carlo simulations demonstrate significant improvements of association accuracy and complex condition adaptability of the proposed algorithm compared with the classical algorithm based on the reference topology feature（ RET）.

关 键 词：航迹关联 时变系统误差 等价量测方程 距离矢量 分级聚类 

分 类 号：TN953[电子电信—信号与信息处理]