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Recursive track-before-detect with target amplitude fluctuations

Recursive track-before-detect with target amplitude fluctuations

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Australian Crown copyright
Published

A particle-based track-before-detect filtering algorithm is presented. This algorithm incorporates the Swerling family of target amplitude fluctuation models in order to capture the effect of radar cross-section changes that a target would present to a sensor over time. The filter is designed with an existence variable, to determine the presence of a target in the data, and an efficient method of incorporating this variable in a particle filter scheme is developed. Results of the algorithm on simulated data show a significant gain in detection performance through accurately modelling the target amplitude fluctuations.

Inspec keywords: radar tracking; tracking filters; radar cross-sections

Other keywords: radar cross-section changes; target amplitude fluctuations; recursive track-before-detect; particle-based track-before-detect filtering algorithm; Swerling family

Subjects: Filtering methods in signal processing; Radar theory

References

    1. 1)
      • W.R. Gilks , C. Berzuini . Following a moving target – Monte Carlo inference for dynamic Bayesian models. J. R. Stat. Soc. Ser. B , 1 , 127 - 146
    2. 2)
    3. 3)
    4. 4)
      • Driessen, H., Boers, Y.: `An efficient particle filter for nonlinear jump Markov systems', Proc. IEE Seminar on Target Tracking: Algorithms and Applications, Mar. 2004, Sussex,, UK.
    5. 5)
      • M.G. Rutten , N.J. Gordon , S. Maskell . Particle-based track-before-detect in Rayleigh noise. Proc. SPIE–Int. Soc. Opt. Eng. , 509 - 519
    6. 6)
      • S.B. Colegrove , A.W. Davis , J.K. Ayliffe . Track initiation and nearest neighbours incorporated into probabilistic data association. J. Electr. Electron. Eng., Aust. , 3 , 191 - 198
    7. 7)
      • Rollason, M., Salmond, D.: `A particle filter for track-before-detect of a target with unknown amplitude', IEE Int. Seminar on Target Tracking: Algorithms and Applications, Oct. 2001, Enschede, Netherlands, p. 14/1–4.
    8. 8)
      • F.J. Harris . On the use of windows for harmonic analysis with the discrete Fouriertransform. Proc. IEEE , 1 , 51 - 83
    9. 9)
    10. 10)
      • Boers, Y., Driessen, J.N.: `Particle filter based detection for tracking', Proc. American Control Conference, June 2001, Arlington, VA, USA, p. 4393–4397.
    11. 11)
      • Boers, Y., Driessen, H.: `A particle filter multi target track before detect application: some special aspects', Fusion 2004: Proc. 7th Int. Conf. on Information Fusion, June 2004, Stockholm, Sweden.
    12. 12)
      • Salmond, D.J., Birch, H.: `A particle filter for track-before-detect', Proc. American Control Conference, June 2001, Arlington, VA, USA, p. 3755–3760.
    13. 13)
      • Boers, Y., Driessen, J.N., Verschure, F., Heemels, W.P.M.H., Juloski, A.: `A multi target track before detect application', CVPR 2003: Proc. IEEE Computer Society Conf. on Computer Vision and Pattern Recognition, June 2003, Madison, WI, USA.
    14. 14)
      • N. Gordon , D. Salmond , A. Smith . Novel approach to nonlinear/non-Gaussian Bayesian state estimation. IEE Proc. F, Radar Signal Process. , 2 , 107 - 113
    15. 15)
      • D. Mušicki , R. Evans , S. Stankovic . Integrated probabilistic data association. IEEE Trans. Autom. Control , 6 , 1237 - 1240
    16. 16)
      • Rutten, M.G., Gordon, N.J., Maskell, S.: `Efficient particle-based track-before-detect in Rayleigh noise', Fusion 2004: Proc. 7th Int. Conf. on Information Fusion, June 2004, Stockholm, Sweden, p. 693–700.
    17. 17)
      • B. Ristic , S. Arulampalam , N. Gordon . (2004) Beyond the Kalman filter: particle filters for tracking applications.
    18. 18)
      • B. Ristic . (2004) Detection and tracking of stealthy targets, Beyond the Kalman filter: particle filters for tracking applications.
    19. 19)
      • P. Swerling . Probability of detection for fluctuating targets. IEEE Trans. Inf. Theory , 2 , 269 - 308
    20. 20)
    21. 21)
      • Andrieu, C., de Freitas, N., Doucet, A.: `Sequential MCMC for Bayesian model selection', Proc. IEEE Signal Processing Workshop on Higher-Order Statistics, June 1999>, Caesarea, Israel, p. 130–134.
    22. 22)
      • M.I. Skolnik . (2001) Introduction to radar systems.
    23. 23)
      • Y. Barniv . Dynamic programming solution for detecting dim moving targets. IEEE Trans. Aerosp. Electron. Syst. , 144 - 156
    24. 24)
    25. 25)
      • C. Berzuini , W. Gilks . (2001) RESAMPLE-MOVE filtering with cross-model jumps, Sequential Monte Carlo methods in practice.
    26. 26)
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