Tracking of maneuvering target by using switching structure and heavy-tailed distribution with particle filter method

IEEE Conference on Control Applications - Proceedings

Volumn 2, Issue , 2002, Pages 1282-1287

  Ikoma, Norikazu ^a   Higuchi, Tomoyuki ^a   Maeda, Hiroshi ^a  

^a KYUSHU INSTITUTE OF TECHNOLOGY   (Japan)

Author keywords

Non Gaussian distribution; Particle filter; Switching structure; Target tracking

Indexed keywords

KALMAN FILTERING; MANEUVERABILITY; MONTE CARLO METHODS; STATE ESTIMATION; STATE SPACE METHODS; SWITCHES; TRACKING (POSITION);

TARGET TRACKING;

AUTOMATIC TARGET RECOGNITION;

EID: 0036031857     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (15)

1. H.Akaike: Likelihood and the Bayes procedure, Bayesian Statistics, Valencia, Spain: University Press, pp.143-166(1980).
2. D.L. Alspach and H.W.Sorenson: Nonlinear Bayesian estimation using Gaussian sum approximations, IEEE Trans.,AC-17, No.4, pp.439-448(1972).
3. Y. Bar-Shalom, K.C. Chang, and H.A.P.Blom: Tracking a maneuvering target using input estimation versus the interacting multiple model algorithm, IEEE Trans., AES-25, No.2, pp.296-300(1985).
4. W.D. Blair and G.A. Watson: IMM algorithm for solution to benchmark problem for tracking maneuvering targets, Proc. of the SPIE, 2221, pp.476-488(1994).
5. H.A.P. Blom and Y.Bar-Shalom: The interacting multiple model algorithm for systems with Markovian switching coefficients, IEEE Trans., AC-33, No.8, pp.780-783(1988).
6. N.J.Gordon, D.J.Salmond, and A.F.M.Smith: Novel approach to nonlinear/non-Gaussian Bayesian State Estimation, IEE Proceedings-F, 140, No.2, pp.107-113(1993).
7. T. Higuchi: Evolutionary time series model with parallel computing, Proc. of The 3rd JAPAN-US Joint Seminar on Statistical Time Series Analysis, pp.183190(2001).
8. N. Ikoma, N. Ichimura, T. Higuchi, and H. Maeda: Particle filter based method for maneuvering target tracking, Proc. of IEEE International Workshop on Intelligent Signal Processing, pp.3-8(2001).
9. M. Isard and A. Blake: Condensation - Conditional density propagation for visual tracking. Journal of Computer Vision, 29, No.1, pp.5-28(1998).
10. G. Kitagawa: "A Nonlinear Smoothing Method for Time Series Analysis", Statistica Sinica, 1, No.2, pp.371-388(1991).
11. G. Kitagawa: A self-organizing state-space model. Journal of the American Statistical Association, 93, 443, pp.1203-1215(1998).
12. G. Kitagawa: Monte Carlo filter and smoother for non-Gaussian nonlinear state space models. Journal of Computational and Graphical Statistics 5, No.1, pp.125(1996).
13. J.S. Liu and R. Chen: Sequential Monte Carlo methods for dynamic systems. Journal of the American Statistical Association, 93, pp.1032-1044(1998).
14. S. McGinnity and G.W. Irwin: Manoeuvring target tracking using a multiple-model bootstrap filter. Sequential Monte Carlo Methods in Practice, Doucet, A., ed., Springer, pp.479-497(2001).
15. R.A. Singer: Estimating optimal tracking filter performance for manned maneuvering targets. IEEE Trans., AES-6, No.4, pp.473-483(1970).