Maneuvering target tracking with modified unbiased FIR filter

被引：0

作者：

Fu, Jinbin ^{[1
]}

Sun, Jinping ^{[1
]}

Lu, Songtao ^{[2
]}

Zhang, Yaotian ^{[1
]}

机构：

[1] School of Electronic and Information Engineering, Beijing University of Aeronautics and Astronautics, Beijing

[2] Department of Electrical and Computer Engineering, Iowa State University, Ames, 50011, IA

来源：

Beijing Hangkong Hangtian Daxue Xuebao/Journal of Beijing University of Aeronautics and Astronautics | 2015年 / 41卷 / 01期

关键词：

Adaptation; Generalized noise power gain; Kalman filter; Maneuvering target tracking; Unbiased finite impulse response filter;

D O I：

10.13700/j.bh.1001-5965.2014.0068

中图分类号：

学科分类号：

摘要：

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. ©, 2015, Beijing University of Aeronautics and Astronautics (BUAA). All right reserved.

引用

页码：77 / 82

页数：5

共 16 条

[1]

Houles A., Bar-Shalom Y., Multisensor tracking of a maneuvering target in clutter, IEEE Transactions on Aerospace and Electronic Systems, 25, 2, pp. 176-188, (1989)

[2]

Bar-Shalom Y., Birmiwal K., Variable dimension filter for maneuvering target tracking, IEEE Transactions on Aerospace and Electronic Systems, 18, 5, pp. 621-629, (1982)

[3]

Magrill D.T., Optimal adaptive estimation of sampled stochastic processes, IEEE Transactions on Automatic Control, 10, 4, pp. 434-439, (1965)

[4]

Nadarajah N., Tharmarasa R., McDonald M., Et al., IMM forward filtering and backward smoothing for maneuvering target tracking, IEEE Transactions on Aerospace and Electronic Systems, 48, 3, pp. 2673-2678, (2012)

[5]

Gibbs B., Advanced Kalman Filtering, Least-squares and Modeling, (2011)

[6]

Shmaliy Y.S., An unbiased FIR filter for TIE model of a local clock in applications to GPS-based timekeeping, IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 53, 5, pp. 862-869, (2006)

[7]

Kwon O.K., Kwon W.H., Lee K.S., FIR filters and recursive forms for discrete-time state-space models, Automatica, 25, 5, pp. 715-728, (1989)

[8]

Kwon W.H., Kim P.S., Han S.H., A receding horizon unbiased FIR filter for discrete-time state space models, Automatica, 38, 3, pp. 545-551, (2002)

[9]

Kwon W.H., Kim P.S., Park P., A receding horizon Kalman FIR filter for discrete time-invariant systems, IEEE Transactions on Automatic Control, 44, 9, pp. 1787-1791, (1999)

[10]

Shmaliy Y.S., An iterative Kalman-like algorithm ignoring noise and initial conditions, IEEE Transactions on Signal Processing, 59, 6, pp. 2465-2473, (2011)

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