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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

付锦斌, 孙进平, 卢松涛, 等 . 针对机动目标的改进UFIR跟踪算法[J]. 北京航空航天大学学报, 2015, 41(1): 77-82. doi: 10.13700/j.bh.1001-5965.2014.0068
引用本文: 付锦斌, 孙进平, 卢松涛, 等 . 针对机动目标的改进UFIR跟踪算法[J]. 北京航空航天大学学报, 2015, 41(1): 77-82. doi: 10.13700/j.bh.1001-5965.2014.0068
FU Jinbin, SUN Jinping, LU Songtao, et al. Maneuvering target tracking with modified unbiased FIR filter[J]. Journal of Beijing University of Aeronautics and Astronautics, 2015, 41(1): 77-82. doi: 10.13700/j.bh.1001-5965.2014.0068(in Chinese)
Citation: FU Jinbin, SUN Jinping, LU Songtao, et al. Maneuvering target tracking with modified unbiased FIR filter[J]. Journal of Beijing University of Aeronautics and Astronautics, 2015, 41(1): 77-82. doi: 10.13700/j.bh.1001-5965.2014.0068(in Chinese)

针对机动目标的改进UFIR跟踪算法

doi: 10.13700/j.bh.1001-5965.2014.0068
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

Maneuvering target tracking with modified unbiased FIR filter

  • 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.

     

  • [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

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出版历程
  • 收稿日期:  2014-02-24
  • 网络出版日期:  2015-01-20

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