基于外定界椭球集员估计的纯方位目标跟踪

刘玉双; 赵剡; 吴发林

doi:10.13700/j.bh.1001-5965.2016.0196

基于外定界椭球集员估计的纯方位目标跟踪

doi: 10.13700/j.bh.1001-5965.2016.0196

北京航空航天大学仪器科学与光电工程学院, 北京 100083

基金项目:

国家自然科学基金 61233005

国家“973”计划 2014CB744200

详细信息

作者简介:
刘玉双，男，博士研究生。主要研究方向：集员估计与导航定位

赵剡，男，博士，教授，博士生导师。主要研究方向：卫星导航系统理论

吴发林，男，博士，副教授，博士生导师。主要研究方向：卫星导航与气候预测

通讯作者:
赵剡, E-mail:zhaoyan@buaa.edu.cn

中图分类号: V249
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- 被引次数: 0
出版历程
- 收稿日期: 2016-03-11
- 录用日期: 2016-05-20
- 网络出版日期: 2017-03-20

Bearing-only target tracking based on ellipsoidal outer-bounding set-membership estimation

School of Instrumentation Science and Opto-electronics Engineering, Beijing University of Aeronautics and Astronautics, Beijing 100083, China

Funds:

National Natural Science Foundation of China 61233005

National Basic Research Program of China 2014CB744200

摘要

摘要:
针对在未知但有界噪声假设下的双基阵纯方位目标跟踪问题，本文提出了一种基于外定界椭球的集员估计（EOB-SME）跟踪算法。该算法具有类似于Kalman滤波的预测-校正递推更新结构，并且在时间更新和量测更新递推阶段分别有一个加权参数。通过最小化估计误差的Lyapunov函数的上界来求取量测更新递推阶段的加权参数，减少了算法的计算量；同时将非线性系统线性化后所产生的误差用椭球进行外包，与量测噪声椭球组成新的噪声椭球。仿真结果表明：在有界噪声假设下，本文所提出算法对纯方位机动目标的跟踪精度更高。
- 集员估计 /
- Kalman滤波 /
- 外定界椭球 /
- 状态估计 /
- 纯角度机动目标跟踪
Abstract:
For the problem of bearing-only maneuvering target tracking under the unknown-but-bounded noises, this paper proposes an ellipsoidal outer-bounding set-membership estimation (EOB-SME) algorithm. The EOB-SME algorithm has a prediction-correction structure in time update and observation update, which is similar to Kalman filter. For each update, there is a data-depending weighting factor. The observation weighting factor is computed by minimizing the upper bound on a Lyapunov function of the estimation error and the computation load is decreased. The linearization errors of nonlinear observation equation are bounded by an ellipsoid which is combined with the ellipsoid of observation noise to form a new ellipsoid of observation noise. The simulation results show that the proposed algorithm has higher tracking accuracy for bearing-only maneuvering target under the assumption of unknown-but-bounded noise.
- set-membership estimation /
- Kalman filter /
- ellipsoidal outer-bounding /
- state estimation /
- bearing-only maneuvering target tracking

HTML全文

图 1 未知但有界噪声下双基阵纯方位机动目标跟踪

Figure 1. Bearing-only maneuvering target tracking with two sensors under unknown-but-bounded noises

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图 2 双基阵纯方位测量下的量测更新过程

Figure 2. Observation update process under bearing-only measurement with two sensors

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图 3 未知但有界的量测噪声

Figure 3. Unknown-but-bounded measurement noises

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图 4 机动目标的速度和加速度曲线

Figure 4. Maneuvering target velocity and acceleration curves

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图 5 不同算法对目标在x方向上的位置、速度和加速度均方根误差曲线

Figure 5. Position, velocity and acceleration root mean square error curves of target in the x direction for different algorithms

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图 6 不同算法对目标在y方向上的位置、速度和加速度均方根误差曲线

Figure 6. Position, velocity and acceleration root mean square error curves of target in the y direction for different algorithms

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图 7 机动目标的位置和椭球可行集

Figure 7. Position of maneuvering target and ellipsoid feasible set

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表 1 目标状态估计的平均均方根误差

Table 1. Average RMSE of target state estimation

算法	位置/m		速度/(m·s^-1)		加速度/(m·s^-2)
	x方向	y方向	x方向	y方向	x方向	y方向
	EKF	0.334	0.267	0.752	0.370	2.983	0.415
CKF	0.324	0.141	0.610	0.374	0.999	0.314
EOB-SME	0.329	0.121	0.567	0.272	0.550	0.284

下载: 导出CSV

表 2 每次递推更新的算法平均运行时间

Table 2. Average run time at each recursive update step for different algorithmsms

ms
算法	EKF	CKF	EOB-SME
运行时间	0.207	2.343	0.791

下载: 导出CSV

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