YU Hongbo, WANG Guohong, CAO Qian, SUN Yun. A Fusion Based Particle Filter TBD Algorithm for Dim Targets[J]. Chinese Journal of Electronics, 2015, 24(3): 590-595. doi: 10.1049/cje.2015.07.026
Citation: YU Hongbo, WANG Guohong, CAO Qian, SUN Yun. A Fusion Based Particle Filter TBD Algorithm for Dim Targets[J]. Chinese Journal of Electronics, 2015, 24(3): 590-595. doi: 10.1049/cje.2015.07.026

A Fusion Based Particle Filter TBD Algorithm for Dim Targets

doi: 10.1049/cje.2015.07.026
Funds:  This work is supported by the National Natural Science Foundation of China (No.61372027, No.61179018).
  • Received Date: 2012-10-11
  • Rev Recd Date: 2014-11-18
  • Publish Date: 2015-07-10
  • A Fusion based particle filter track-beforedetect algorithm (FPF-TBD) is proposed for dealing with dim targets, in which the importance density function of the Particle filter (PF) is generated by means of a fusion algorithm. In order to construct an accurate approximation to the true proposal distribution, the state at each time scan is predicted according to the Extended Kalman filter algorithm (EKF) and the Unscented Kalman filter (UKF) simultaneously. The information, based on the recursion of the weights, is gathered over multiple scans, and the detection decision is made based on tracking results at the end of the processing chain. By making best use of the recent measurements, this new proposed method can obtain an accurate approximation to the system and as a result, improve the track accuracy and detection performance. Simulation results illustrate the effectiveness of this approach.
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  • E. Grossi, M. Lops and L. Venturino, "A heuristic algorithm for track-before-detect with thresholded observations in radar systems", IEEE Signal Processing Letters, Vol.20, No.8, pp.811- 814, 2013.
    Y.Y. Zhang and C.X. Wang, "Space small targets detection based on improved DPA", Acta Electronica Sinica, Vol.38, No.3, pp.556-560, 2010. (in Chinese)
    S.C. Tan and G.H. Wang, "Joint range ambiguity resolving and multiple maneuvering targets tracking in clutter via MMPHDFDA", Science China Information Series, Vol.57, No.8, pp.1-12, 2014.
    Z. Wu, J. Zhang, L. Zhang, et al., "A novel Hough track initialization algorithm for multi-sensor environment", Sensor Letters, Vol.11, No.4, pp.686-691, 2013.
    D.J. Salmond and H. Birch, "A particle filter for track-beforedetect", Proc. of the American Control Conference, Arlington, USA, pp.3755-3760, 2001.
    G.H. Wang, S.C. Tan, C.B. Guan, et al., "Multiple model particle filter track-before-detect for range ambiguous radar", Chinese Journal of Aeronautics, Vol.26, No.6, pp.1477-1487, 2013.
    H.T. Su, P.L. Shui and H.W. Liu, "Particle filter based trackbefore- detect algorithm for over-the-horizon radar target detection and tracking", Chinese Journal of Electronics, Vol.18, No.1, pp.59-64, 2009.
    L.H. Se, S. Zhang and H.Y. Wang, "A T-wave alternans detection algorithm based on particle filtering in non-Gaussian environment", Acta Electronica Sinica, Vol.42, No.2, pp.223-229, 2014. (in Chinese)
    S. Gao S, D.Y. Bi and N. Wei, "Small target track beforedetect algorithm based on unscented particle filtering", Journal of Computer Applications, Vol.29, No.8, pp.2060-2064, 2009. (in Chinese)
    D. Huang, A. Xue and Y. Guo, "A particle filter track-beforedetect algorithm for multi-radar system", Electronics and Electrical Engineering, Vol.19, No.5, pp.3-8, 2013.
    J. Lu, P.L. Shui and H.T. Su, "Track-before-detect method based on cost-reference particle filter in non-linear dynamic systems with unknown statistics", IET Signal Processing, Vol.8, No.1, pp.85-94, 2014.
    R.T. Sukhavasi and B. Hassibi, "The Kalman-like particle filter: Optimal estimation with quantized innovations/ measurements", IEEE Transactions on Signal Processing, Vol.61, No.1, pp.131-136, 2013.
    Y. He, G.H. Wang and D.J. Lu, Multisensor Information Fusion with Applications, Publishing House of Electronics Industry, Beijing, China, 2010. (in Chinese)
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