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状态自适应无迹卡尔曼滤波算法及其在水下机动目标跟踪中的应用
引用本文:马艳,刘小东.状态自适应无迹卡尔曼滤波算法及其在水下机动目标跟踪中的应用[J].兵工学报,2019,40(2):361-368.
作者姓名:马艳  刘小东
作者单位:(1.西北工业大学 航海学院, 陕西 西安 710072; 2.中国舰船研究设计中心, 湖北 武汉 430061)
基金项目:国家自然科学基金项目(61531015、61301197);水声对抗技术国防科技重点实验室基金项目(2016年)
摘    要:为了满足水下对抗对机动目标实时跟踪和目标航速、航向准确估计的要求,针对观测量为距离和方位的机动目标跟踪,对传统无迹卡尔曼滤波(UKF)跟踪算法进行了改善。提出根据UKF算法预测值和观测值残差的概率分布自适应调整目标状态噪声方法,使得UKF跟踪算法能够根据目标运动状态及时调整状态方程,在目标机动时减小对预测值的依赖,在目标非机动时增大对预测值的依赖。这种在线实时估计系统噪声状态的跟踪方法更加适用于机动目标的跟踪。数值仿真结果表明:该算法不仅在目标机动时具有良好的跟踪效果,而且在目标非机动时具有准确的估计性能。通过声纳信息综合处理系统验证了状态自适应UKF跟踪算法的性能。

关 键 词:水下机动目标跟踪  无迹卡尔曼滤波  自适应滤波  航速  航向  
收稿时间:2018-02-06

State Adaptive Unscented Kalman Filter Algorithm and Its Application in Tracking of Underwater Maneuvering Target
MA Yan,LIU Xiaodong.State Adaptive Unscented Kalman Filter Algorithm and Its Application in Tracking of Underwater Maneuvering Target[J].Acta Armamentarii,2019,40(2):361-368.
Authors:MA Yan  LIU Xiaodong
Affiliation:(1.School of Marine Science and Technology, Northwestern Polytechnical University, Xi'an 710072, Shaanxi, China; 2.China Ship Development and Design Center, Wuhan 430061, Hubei, China)
Abstract:In order to meet the needs for tracking the underwater maneuvering target in real-time and predicting its location in the underwater confrontation environment, the higher requirements are put forward for accurate and fast estimation of target’s sailing speed and course. The traditional unscented Kalman filter(UKF) tracking algorithm is improved for maneuvering target tracking with range and azimuth. The improved tracking algorithm can also be used to estimate the system status noise online in real-time without determining the state equation and the state noise variance in advance, thus tracking the maneuvering target adaptively. A novel adaptive UKF method is proposed,which adaptively adjusts the target state noise with residual probability distribution according to the residuals of predicted and observed values of UKF algorithm, so that the UKF tracking algorithm can adjust the state according to the target state the equation reduces the reliance on the predicted value when the target is maneuvering and increases the reliance on the prediction when the target is maneuvering. Primary numerical simulation results show that the algorithm not only has good tracking performance in target maneuvering but also has accurate estimation in non-maneuvering. At last, the tracking performance of state adaptive UKF algorithm is illustrated by sonar simulation system.
Keywords:underwater maneuvering target tracking  unscented Kalman filter  adaptive filter  speed  course  
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