基于动态估计反馈的灰色理论航迹关联算法  

Grey theory track association algorithm based on dynamic estimation feedback

作　　者：邱建杰 蔡益朝[1] 李浩[1] 黄权印 QIU Jianjie;CAI Yichao;LI Hao;HUANG Quanyin(Air Force Early Warning Academy,Wuhan 430014,China)

机构地区：[1]空军预警学院,湖北武汉430014

出　　处：《系统工程与电子技术》2024年第4期1401-1411,共11页Systems Engineering and Electronics

基　　金：国家自然科学基金(61502522)资助课题。

摘　　要：针对传统基于灰色理论航迹关联算法未充分利用航迹历史信息,在密集目标以及时变噪声协方差等场景下关联正确率下降问题,在传统灰色分析法基础上增加了一种动态估计反馈机制。改进后的算法引入了Sage-Husa估计器来实时估计传感器的噪声协方差作为评估输出数据质量的依据,并使用Critic赋权法将实时噪声协方差转换为各时刻序贯权重,从而保证最大程度上的利用航迹历史信息。仿真证明,在密集目标与时变噪声协方差等特殊关联场景下,所提算法明显优于传统灰色分析法以及模糊法、加权法等经典算法,充分证明了所提算法的性能优越性以及鲁棒性。In response to the problem that traditional grey theory based track association algorithms do not make full use of track history information and reduce association accuracy in scenarios such as dense targets and time-varying noise covariance,a dynamic estimation feedback mechanism based on traditional grey analysis methods is added.The improved algorithm introduces Sage-Husa estimator to estimate real-time sensor noise covariance as a basis for evaluating output data quality,and uses Critical weighting method to convert real-time noise covariance into sequential weights at each time,ensuring maximum utilization of track history information.Simulation has shown that in special correlation scenarios such as dense targets and time-varying noise covariance,the proposed algorithm is significantly superior to traditional grey analysis methods,as well as classical algorithms such as fuzzy and weighted methods,fully demonstrating the performance superiority and robustness of the proposed algorithm.

关 键 词：航迹关联 多传感器 数据融合 灰色理论 

分 类 号：TN953[电子电信—信号与信息处理] TN957.51[电子电信—信息与通信工程]