Advanced Search
Volume 41 Issue 1
Jan.  2015
Turn off MathJax
Article Contents
Journal of Beijing University of Aeronautics and Astronautics  > 2015  >  41(1): 77-82.
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)
shu

Maneuvering target tracking with modified unbiased FIR filter

doi: 10.13700/j.bh.1001-5965.2014.0068
  • 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

  • Received Date: 24 Feb 2014
  • Publish Date: 20 Jan 2015
    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.

     

    • FullText(HTML)
  • loading
    • References(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
    • Relative Articles
    • Supplements(0)
    • Cited By
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索
    Get Citation
    PDF
    XML

    Article Metrics

    Article views(1223) PDF downloads(603) Cited by()
    Proportional views
    Related

    Copyright © Journal of Beijing University of Aeronautics and Astronautics

    Address: Editorial Department of Journal of Beijing University of Aeronautics and Astronautics, 37 Xueyuan Road, Haidian District, Beijing Post Code: 100191 Email: jbuaa@buaa.edu.cn

    Supported by: Beijing Renhe Information Technology Co., Ltd.    

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return