EKF和UKF算法在双观测站纯方位目标跟踪中的应用  

Applications of EKF and UKF Algorithms in Bearings-only Target Trackingwith a Double Observation Stations

作　　者：成春彦 李亚安[1] CHENG Chunyan;LI Yaan(School of Marine Science and Technology,Northwestern Polytechnical University,Xi’an 710072,China)

机构地区：[1]西北工业大学航海学院,陕西西安710072

出　　处：《水下无人系统学报》2023年第3期388-397,共10页Journal of Unmanned Undersea Systems

基　　金：国家自然科学基金项目资助(11874302)。

摘　　要：为了对水下运动目标进行实时跟踪,以静止双观测站纯方位跟踪系统为研究对象,分别结合扩展卡尔曼滤波(EKF)算法和无迹卡尔曼滤波(UKF)算法的原理,对基于EKF和UKF算法的双观测站纯方位跟踪系统进行了仿真分析及比较。结果表明,基于2种算法的双观测站纯方位系统都能适用于水下运动目标实时跟踪,但后者具有更快的收敛速度和更好的鲁棒性。同时,分别分析了双站距离和方位角量测误差对实时跟踪效果的影响,仿真结果表明, 2个观测站距离过近或过远都会降低目标跟踪的效果,基于EKF和UKF算法的双观测站系统在两站距离800 m时都能得到较满意的跟踪效果;随着方位角量测误差的增大,基于2种算法的双观测站系统的跟踪性能都会下降,但UKF算法在EKF算法跟踪失效时仍然具有较好的跟踪性能。For tracking underwater moving targets in real time,a bearings-only tracking system with a stationary doubleobservation station was investigated.By combining the extended Kalman filter(EKF)algorithm and the unscented Kalmanfilter(UKF)algorithm,the bearings-only tracking system based on the EKF and UKF algorithms was simulated and compared.The results demonstrated that the double observation station system based on the two algorithms can be applied to real-timetracking of underwater moving targets,but the latter shows faster convergence and better robustness.The influence of thedistance between the two stations and bearings measurement error on the real-time tracking effect was also analyzed.Simulation results showed that the effect of target tracking is reduced if the distance between the two observation stations istoo small or too large.The double observation stations system based on EKF and UKF algorithms can achieve satisfactorytracking results when the distance between the two stations is 800 m;with the increase in the bearing measurement error,thetracking performance of the double observation stations system based on the two algorithms decreases,but the UKF algorithmstill exhibits better tracking performance when the EKF algorithm fails to track.

关 键 词：水下运动目标 双观测站 纯方位目标跟踪 扩展卡尔曼滤波 无迹卡尔曼滤波 

分 类 号：TJ630.1[兵器科学与技术—武器系统与运用工程] U764.7[交通运输工程]