一种滑动后向递推的EKF纯方位目标跟踪方法

郑 艺; 王明洲

doi:10.11993/j.issn.2096-3920.2020.06.011

一种滑动后向递推的EKF纯方位目标跟踪方法

doi: 10.11993/j.issn.2096-3920.2020.06.011

郑艺,
王明洲

中国船舶重工集团公司第705研究所, 陕西西安, 710077

详细信息

作者简介:
郑艺(1992-), 女, 在读博士, 主要研究方向为水下目标跟踪定位.

中图分类号: TJ630.34 U664.82
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- 文章访问数: 711
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- 被引次数: 0
出版历程
- 收稿日期: 2020-07-14
- 修回日期: 2020-09-27
- 刊出日期: 2020-12-31

Sliding Backward Recursive EKF Bearings-Only Target Tracking Method

The 705 Research Institute, China Shipbuilding Industry Corporation, Xi’an 710077, China

摘要

摘要: 由于只有一个观测点且只能获取目标方位信息, 被动的单站纯方位水下目标跟踪是定位跟踪领域的难点之一。在工程应用中有时存在观测时间短、数据量小的情况, 进一步加大了定位跟踪的难度。基于此, 文中研究了常规扩展卡尔曼滤波(EKF)原理, 分析了其在单站纯方位目标跟踪中状态估计变化的特点, 并通过公式推导进行了证明。针对短时观测、小数据量的特殊背景, 提出了一种滑动后向递推的EKF方法, 通过后向递推与正向递推的结合, 增加对数据的反复利用, 降低了估计误差。仿真试验结果证明, 在不同观测噪声、不同噪声协方差估计的情况下, 对于短时观测小数据量下的单站纯方位目标跟踪, 文中方法比常规EKF方法具有更低的误差。
- 水下目标跟踪 /
- 后向递推 /
- 扩展卡尔曼滤波 /
- 纯方位 /
- 短时观测
Abstract: In the field of target location and tracking, when only one observer is present and only the bearings of the target can be obtained, passive bearings-only underwater target tracking by a single observer is difficult. In engineering applications, the time of observation is short and the amount of data is sometimes small, which makes target location and tracking more difficult. In this study, the principle of a conventional extended Kalman filter(EKF) is studied and the characteristics of state estimation changes in bearings-only target tracking by a single observer are analyzed and proved by formula derivation. Considering the special background of short-term observation and the existence of a small amount of data, this study proposes a sliding backward recursive EKF method. Through a combination of backward and forward recursion, the data are reused and estimation errors are reduced. In a simulation of different observation noises and noise covariance estimates, results show that the proposed method generates lower errors than the conventional EKF for bearings-only target tracking by a single observer using a small amount of short-term observation data.
- underwater target tracking /
- backward recursion /
- extended Kalman filter(EKF) /
- bearings-only /
- short-term observation

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参考文献(15)

[1]	刘忠, 周丰, 石章松, 等. 纯方位目标运动分析[M]. 北京: 国防工业出版社, 2009.
[2]	Miller A B, Miller B M. Underwater Target Tracking Using Bearing-Only Measurements[J]. Journal of Communications Technology and Electronics, 2018, 63(6): 643-649.
[3]	孙仲康, 郭福成, 冯道旺, 等. 单站无源定位跟踪技术[M]. 北京: 国防工业出版社, 2008.
[4]	Shalom Y, Li X R, Thiagalingam K. Estimation with Applications to Tracking and Navigation[M]. New York: Wiley, 2001: 381-394.
[5]	Julier S J, Uhlmann J K. Unscented Filtering and Nonlinear Estimation[J]. Proceedings of the IEEE, 2004, 92(3): 401-422.
[6]	Arasaratnam I, Haykin S. Cubature Kalman Filters[J]. IEEE Transactions on Automatic Control, 2009, 54(6): 1254-1269.
[7]	Gordon N J, Salmond D J, Smith A F M. Novel Approach to Nonlinear/non-Gaussian Bayesian State Estimate[J]. IEEE Proceeding of Radar, Sonar and Navigation, 1993, 140(2): 107-113.
[8]	Zhao J. Dynamic State Estimation with Model Uncertainties Using H∞ Extended Kalman Filter[J]. IEEE Transactions on Power Systems, 2018, 33(1): 1099-1100.
[9]	Li X, Zhao C, Yu J, et al. Underwater Bearing-only and Bearing-Doppler Target Tracking Based on Square Root Unscented Kalman Filter[J]. Entropy, 2019, 21(8): 740.
[10]	Lim J, Shin M, Hwang W. Variants of Extended Kalman Filtering Approaches for Bayesian Tracking[J]. International Journal of Robust and Nonlinear Control, 2016, 27(2): 319-346.
[11]	He R, Chen S X, Wu H, et al. Adaptive Covariance Feedback Cubature Kalman Filtering for Continuous-Discrete Bearings-Only Tracking System[J]. IEEE Access, 2019, 7: 2686-2694.
[12]	Zhou W, Liu L. Adaptive Cubature Kalman Filter Based on the Expectation-Maximization Algorithm[J]. IEEE Access, 2019, 7: 158198-158206.
[13]	Zhao Y, Wang T, Qin W, et al. Improved Rao-Blackwell- ised Particle Filter Based on Randomly Weighted Particle Swarm Optimization[J]. Computers and Electrical Engineering, 2018, 71: 477-484.
[14]	Liu H, He B, Feng C, et al. Navigation Algorithm Based on PSO-BP UKF of Autonomous Underwater Vehicle[C]// IEEE Underwater Technology(UT). Kaohsiung, Taiwan: IEEE, 2019.
[15]	Konatowski S, Kaniewski P, Matuszewski J. Comparison of Estimation Accuracy of EKF, UKF and PF Filters[J]. Annual of navigation, 2016, 23(1): 69-87.