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Multirate interacting multiple model particle filter for terrain-based ground target tracking

Multirate interacting multiple model particle filter for terrain-based ground target tracking

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Ground target tracking is a nonlinear filtering problem when it incorporates terrain and road constraints into system modelling and uses polar coordinate sensing. Furthermore, when tracking ground manoeuvring targets with an interacting multiple model approach, a non-Gaussian problem exists because of an inherent mixing operation. A multirate interacting multiple model particle filter (MRIMM-PF) is presented to effectively solve the problem of nonlinear and non-Gaussian tracking, with an emphasis on computational savings. The sample subset of each mode is updated at a different rate and mode switches are performed according to a Markov chain at a low rate. For a fixed number of samples, simulation results show that the MRIMM-PF significantly reduces computational costs, with comparable tracking performance to multiple model particle filter.

References

    1. 1)
      • Garren, D., Chong, C., Grayson, T.P.: `Ground target tracking – a historical perspective', Proc. IEEE Aerospace 2000, 2000, Big Sky, Montana.
    2. 2)
      • K. Kastella , C. Kreucher . Multiple model nonlinear filtering for low signal ground target applications. IEEE Trans. Aerosp. Electron. Syst. , 2 , 549 - 564
    3. 3)
      • T. Kirubarajan , Y. Bar-Shalom , K.R. Pattipati , I. Kadar . Ground target tracking with topography-based variable structure IMM estimator. Proc. SPIE – Signal Data Process. Small Targets , 222 - 233
    4. 4)
      • Shea, P.J., Zadra, T.: `Precision tracking of ground targets', Proc. IEEE Aerospace 2000, 2000, Big Sky, Montana.
    5. 5)
    6. 6)
    7. 7)
      • Y. Bar-Shalom , X.-R Li . (1993) Estimation and tracking: principles, techniques, and software.
    8. 8)
    9. 9)
      • S. Julier , J. Uhlmann , H.F. Durrant-Whyte . A new method for the nonlinear transformation of means and covariance in filters and estimators. IEEE Trans. Autom. Control , 3 , 477 - 482
    10. 10)
      • A. Doucet , N. de Freitas , N. Gordon . (2001) Sequential Monte Carlo methods in practice.
    11. 11)
      • N. Gordon , D. Salmond , A. Smith . Novel approach to nonlinear/non-Gaussian Bayesian state estimation. IEE Proc. F , 2 , 107 - 113
    12. 12)
      • Blom, H.A.P., Bloem, E.A.: `Joint IMMPDA particle filter', Proc. 6th Int. Conf. on Information Fusion, July 2003, Cairns, Queensland, Australia.
    13. 13)
    14. 14)
      • D. Fox . KLD-sampling: adaptive particle filter. Adv. Neural Inform. Process. Syst. (NIPS)
    15. 15)
    16. 16)
    17. 17)
    18. 18)
    19. 19)
    20. 20)
      • Merwe, R.V.D., Doucet, A., Freitas, N.D., Wan, E.: `The unscented particle filter', Technical Report CUED/TR 380, 16 August 2000.
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