针对机动目标的改进UFIR跟踪算法

付锦斌; 孙进平; 卢松涛; 张耀天

doi:10.13700/j.bh.1001-5965.2014.0068

针对机动目标的改进UFIR跟踪算法

doi: 10.13700/j.bh.1001-5965.2014.0068

1.
北京航空航天大学电子信息工程学院, 北京 100191
2. 爱荷华州立大学电子与计算工程系, 安姆斯 50011

基金项目: 国家自然科学基金资助项目(61201318,61471019);国防重点实验室基金资助项目(9140C800202120C80279)

详细信息

作者简介:
付锦斌(1991-),男,江西景德镇人,博士生,by1302155@ee.buaa.edu.cn

通讯作者:
孙进平(1975-),男,甘肃天水人,教授, sunjinping@buaa.edu.cn,主要研究方向为雷达信号处理.

中图分类号: TN953
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出版历程
- 收稿日期: 2014-02-24
- 网络出版日期: 2015-01-20

Maneuvering target tracking with modified unbiased FIR filter

1.
School of Electronic and Information Engineering, Beijing University of Aeronautics and Astronautics, Beijing 100191, China
2. Department of Electrical and Computer Engineering, Iowa State University, Ames, Iowa 50011, USA

摘要

摘要: 在机动目标跟踪中,卡尔曼滤波器(KF)及其改进算法的性能依赖于过程噪声统计特性的准确性,若模型过程噪声与实际存在偏差,通常会出现估计误差增大甚至发散的现象.无偏有限冲击响应滤波器(UFIR)在滤波过程中无需过程噪声统计特性的先验知识,将其应用于机动目标跟踪中,针对现有UFIR滤波器中广义噪声功率增益(GNPG)不随量测新息变化的问题,设计了一种根据相邻时刻量测新息比值动态调整GNPG的改进UFIR滤波器,改善了UFIR滤波器的机动检测能力.仿真结果表明,当假定过程噪声准确时,现有和改进UFIR滤波器与KF的跟踪性能相似;但当假定过程噪声不准确时,改进UFIR滤波器具有最佳的滤波效果.
- 机动目标跟踪 /
- 无偏有限冲击响应滤波器 /
- 卡尔曼滤波器 /
- 广义噪声功率增益 /
- 自适应
Abstract: In the field of maneuvering target tracking, the performance of Kalman filter(KF)and its variants is dependeds on the accuracy of the assumed process noise statistics. If the assumed process noise is not accurate, the performance of the KF and its improved algorithms will be degraded significantly. In some cases, the filters might even cannot be converged. Unbiased finite impulse response (UFIR) filter does not need the prior knowledge of the process noise statistics during filtering. Hence, it can be utilized to overcome the problem of the inaccurate assumed process noise statistics to realize the maneuvering target tracking. Since the generalized noise power gain (GNPG) of the existing UFIR filter cannot be adapted to the measurements innovation, an improved UFIR filter was proposed. The proposed UFIR dynamically adjusts GNPG according to the ratio of measurements innovations between the adjacent time such that it can improve the detecting ability of the UFIR filter for target maneuver. The simulation results illustrate that if assumed process noise is accurate, the performance of the existing UFIR filter and the proposed FIR filter is similar to KF; but if assumed process noise is not accurate, the performance of the proposed UFIR shows better than the other ones.
- maneuvering target tracking /
- unbiased finite impulse response filter /
- Kalman filter /
- generalized noise power gain /
- adaptation

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参考文献(16)

[1]	Houles A,Bar-Shalom Y.Multisensor tracking of a maneuvering target in clutter[J].IEEE Transactions on Aerospace and Electronic Systems,1989,25(2):176-188.
[2]	Bar-Shalom Y,Birmiwal K.Variable dimension filter for maneuvering target tracking[J].IEEE Transactions on Aerospace and Electronic Systems,1982,18(5):621-629.
[3]	Magrill D T.Optimal adaptive estimation of sampled stochastic processes[J].IEEE Transactions on Automatic Control,1965,10(4):434-439.
[4]	Nadarajah N,Tharmarasa R,McDonald M,et al.IMM forward filtering and backward smoothing for maneuvering target tracking[J].IEEE Transactions on Aerospace and Electronic Systems,2012,48(3):2673-2678.
[5]	Gibbs B.Advanced Kalman filtering,least-squares and modeling[M].New York:Wiley,2011.
[6]	Shmaliy Y S.An unbiased FIR filter for TIE model of a local clock in applications to GPS-based timekeeping[J].IEEE Transactions on Ultrasonics,Ferroelectrics,and Frequency Control,2006,53(5):862-869.
[7]	Kwon O K,Kwon W H,Lee K S.FIR filters and recursive forms for discrete-time state-space models[J].Automatica,1989,25(5):715-728.
[8]	Kwon W H,Kim P S,Han S H.A receding horizon unbiased FIR filter for discrete-time state space models[J].Automatica,2002,38(3):545-551.
[9]	Kwon W H,Kim P S,Park P.A receding horizon Kalman FIR filter for discrete time-invariant systems[J].IEEE Transactions on Automatic Control,1999,44(9):1787-1791.
[10]	Shmaliy Y S.An iterative Kalman-like algorithm ignoring noise and initial conditions[J].IEEE Transactions on Signal Processing,2011,59(6):2465-2473.
[11]	Ramirez-Echeverria F,Sarr A,Shmaliy Y S.Optimal memory for discrete-time FIR filters in state-space[J].IEEE Transactions on Signal Processing,2014,62(3):557-561.
[12]	Song T L,Speyer J L.A stochastic analysis of a modified gain extended Kalman filter with application to estimation with bearing only measurements[J].IEEE Transactions on Automatic Control,1985,AC-30(10):940-949.
[13]	Shmaliy Y S,Ibarra-Manzano O.Time-variant linear optimal finite impulse response estimator for discrete-time state-space models[J].International Journal of Adaptive Control and Signal Processing,2012,26(2):95-104.
[14]	Shmaliy Y S.Linear optimal FIR estimation of discrete time-invariant state-space models[J].IEEE Transactions on Signal Processing,2010,58(6):3086-3096.
[15]	Shmaliy Y S,Simon D.Iterative unbiased FIR state estimation: a review of algorithms[J].Eurasip Journal on Advances in Signal Processing,2013(1):1-16.
[16]	Simon D,Shmaliy Y S.Unified forms for Kalman and finite impulse response filtering and smoothing[J].Automatica,2013,49(6):1892-1899