一种面向纯方位机动目标跟踪的伪线性卡尔曼滤波方法  

A pseudo linear Kalman filtering algorithm for bearings-only maneuvering target tracking

作　　者：刘锡楠 吴盘龙[1] 薄煜明[1] LIU Xinan;WU Panlong;BO Yuming(School of Automation,Nanjing University of Science&Technology,Nanjing 210094,China;China North Vehicle Research Institute,Beijing 100000,China)

机构地区：[1]南京理工大学自动化学院,江苏南京210094 [2]中国北方车辆研究所,北京100000

出　　处：《指挥控制与仿真》2024年第5期62-68,共7页Command Control & Simulation

基　　金：航空科学基金(2022Z037059001、20220001059001);上海航天科技创新基金(SAST2021-027、SAST2021-056)。

摘　　要：针对单站纯方位地面机动目标跟踪问题,提出一种改进距离参数化工具变量伪线性卡尔曼滤波算法。首先基于观测站探测范围,利用距离参数化将目标与观测站的相对距离划分为若干个子区间,并独立运行工具变量伪线性卡尔曼滤波器。然后基于滤波新息与新息协方差更新子滤波器权重并检测目标机动,通过重置子滤波器的权重和状态信息保证滤波稳定。最后将各子滤波器状态信息加权融合获取目标的状态,解决目标初始距离未知以及目标机动导致跟踪精度下降的问题。仿真结果显示,本文提出的算法的位置跟踪精度比传统方法有显著提高,能有效实现对机动目标的跟踪。A modified range parameterized instrumental variable pseudo linear Kalman filter(MRP-IVPLKF)algorithm is proposed for single station bearings-only ground maneuvering target tracking.Firstly,based on the detection range of the observation station,the relative range between the target and the observation station is divided into several sub-intervals using range parameterization,and instrumental variable pseudo linear Kalman filters are independently operated.Then,based on the filtering innovation and innovation covariance,the sub-filter weights are updated and target maneuvering is detected,ensuring filter stability by resetting the sub-filter weights and state information.Finally,the state information of each sub-filter is weighted and fused to obtain the target state,solving the problem of the decrease in tracking accuracy caused by unknown initial range and target maneuvering.The simulation results show that the position tracking accuracy of the algorithm proposed in this article is significantly improved compared to traditional methods,and can effectively achieve tracking of maneuvering targets.

关 键 词：距离参数化 工具变量 伪线性滤波 目标跟踪 机动检测 

分 类 号：E211[军事—军队指挥学]