雷达机动目标跟踪算法的研究

发布时间：2018-03-19 21:16

本文选题：机动目标　切入点：卡尔曼滤波　出处：《大连海事大学》2017年硕士论文　论文类型：学位论文

【摘要】：随着海上航运事业的不断发展,海上船舶的数量越来越多,船舶航行时发生机动的可能性变大,海上的交通环境变得越发复杂,因此,对雷达机动目标跟踪的精度与稳定性的要求也会越来越高。在对机动目标跟踪的系统中,滤波算法就是其核心技术之一。本文对滤波算法进行了研究,提出了一些改进方法。本文首先阐述了机动目标跟踪的基本原理,分析了 CV与CA模型,Singer模型与"当前"统计模型等机动模型,并通过仿真结果分析Singer模型与"当前"统计模型对机动目标跟踪建模时效果的优劣。并分析目标发生机动时,不同的机动频率的滤波均方误差不同,为进一步研究滤波方法打下基础。其次,对卡尔曼滤波算法、扩展卡尔曼滤波算法与自适应卡尔曼滤波算法的机动目标跟踪性能进行了仿真对比分析,通过均方误差分析各自的跟踪误差特性。接着,基于"当前"统计模型分别介绍了自适应扩展卡尔曼滤波器,分析其滤波效果的优缺点。通过研究得出基于机动频率的自适应扩展卡尔曼算法,并证明该算法的可行性。提出了一种改进的自适应扩展卡尔曼滤波算法,通过仿真证明了算法有效性。最后,介绍了 Sage-Husa自适应滤波算法与简化的Sage-Husa自适应滤波算法,针对加权系数的计算方法做出改进,提出一种基于加权补偿的Sage-Husa自适应卡尔曼滤波算法,接着将加权系数的算法与之前得出的自适应机动频率算法加入到改进的自适应卡尔曼滤波算法中,得出一种基于加权补偿的自适应扩展卡尔曼滤波算法,通过仿真证明了算法有效性。
[Abstract]:With the continuous development of maritime navigation, the number of ships at sea becomes more and more, the possibility of ship maneuvering becomes greater, and the maritime traffic environment becomes more and more complex. In the system of maneuvering target tracking, the filtering algorithm is one of the core technologies. Some improved methods are put forward. Firstly, the basic principle of maneuvering target tracking is expounded, and the maneuvering models such as CV and CA model, Singer model and "current" statistical model are analyzed. The effectiveness of Singer model and "current" statistical model for maneuvering target tracking modeling is analyzed by simulation results, and the filtering mean square error of different maneuvering frequency is analyzed when the target is maneuvering. Secondly, the performance of maneuvering target tracking of Kalman filtering algorithm, extended Kalman filter algorithm and adaptive Kalman filter algorithm are compared and analyzed by simulation. Based on the current statistical model, the adaptive extended Kalman filter is introduced. The advantages and disadvantages of the filtering effect are analyzed. The adaptive extended Kalman algorithm based on maneuvering frequency is obtained, and the feasibility of the algorithm is proved. An improved adaptive extended Kalman filter algorithm is proposed. Finally, the Sage-Husa adaptive filtering algorithm and the simplified Sage-Husa adaptive filtering algorithm are introduced, and the calculation method of the weighting coefficient is improved. A Sage-Husa adaptive Kalman filter algorithm based on weighted compensation is proposed, and then the weighted coefficient algorithm and the previous adaptive maneuvering frequency algorithm are added to the improved adaptive Kalman filter algorithm. An adaptive extended Kalman filter algorithm based on weighted compensation is proposed. The simulation results show that the algorithm is effective.
【学位授予单位】：大连海事大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TN953;TN713

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