Similar articles

Abstract: In this study, an unscented particle filtering method based on an interacting multiple model (IMM) frame for a Markovian switching system is presented. The method integrates the multiple model (MM) filter with an unscented particle filter (UPF) by an interaction step at the beginning. The framework (interaction/mixing, filtering, and combination) is similar to that in a standard IMM filter, but an UPF is adopted in each model. Therefore, the filtering performance and degeneracy phenomenon of particles are improved. The filtering method addresses nonlinear and/or non-Gaussian tracking problems. Simulation results show that the method has better tracking performance compared with the standard IMM-type filter and IMM particle filter.

[1] Arulampalam, M.S., Maskell, S., Gordon, N., Clapp, T., 2002. A tutorial on particle filters for online nonlinear non-Gaussian Bayesian tracking. IEEE Trans. on Signal Processing, 50(2):174-188.

[2] Bar-Shalom, Y., Chang, K.C., Blom, H.A.P., 1989. Tracking a maneuvering target using input estimation versus the interacting multiple model algorithm. IEEE Trans. on Aeros. Electron. Syst., 25(2):296-300.

[3] Bar-Shalom, Y., Li, X.R., 1993. Estimation and Tracking: Principles, Techniques, and Software. Artech House.

[4] Bar-Shalom, Y., Challa, S., Blom, H.A.P., 2005. IMM estimator versus optimal estimator for hybrid systems. IEEE Trans. on Aeros. Electron. Syst., 41(3):986-991.

[5] Boers, Y., Driessen, J.N., 2003. Interacting multiple model particle filter. IEE Proc.-Radar Sonar and Navigation, 150(5):344-349.

[6] Chong, C.Y., Garren, D., Grayson, T.P., 2000. Ground Target Tracking—A Historical Perspective. Proc. IEEE Aerospace Conf., 3:433-448.

[7] Cui, N., Hong, L., Layne, J.R., 2005. A comparison of nonlinear filtering approaches with an application to ground target tracking. Signal Processing, 85(8):1469-1492.

[8] Farina, A., Ristic, B., 2002. Tracking a ballistic target: comparison of several nonlinear filters. IEEE Trans. on Aeros. Electron. Syst., 38(3):854-867.

[9] Gordon, N.J., Slamond, D.J., Smith, A.F.M., 1993. Novel approach to nonlinear/non-Gaussian Bayesian state estimation. Radar and Signal Processing, IEE Proc. F, 140(2):107-113.

[10] Hong, L., Cui, N., Bakich, M., Layne, J.R., 2006. Multirate interacting multiple model particle filter for terrain-based ground target tracking. IEE Proc.-Control Theory and Applications, 153:721-731.

[11] Julier, S.J., Uhlmann, J.K., 1997. A New Extension of the Kalman Filter to Nonlinear Systems. Proc. AeroSense: 11th Int. Symp. Aerospace/Defense Sensing, Simulation and Controls. Orlando, p.54-65.

[12] Julier, S.J., 2002. The Scaled Unscented Transformation. Proc. American Control Conf., p.4555-4559.

[13] Kirubarajan, T., Bar-Shalom, Y., Pattipati, K.R., 1998. Tracking Ground Targets with Road Constraints Using an IMM Estimator. IEEE Proc. on Aerospace Conf., 5:5-12.

[14] Kreucher, C., Kastella, K., 2001. Multiple-model Nonlinear Filtering for Low-signal Ground Target Applications. In: Kadar, I. (Ed.), Signal Processing Sensor Fusion, and Target Recognition X. Proc. SPIE, 4380:1-12.

[15] Mazor, E., Averbuch, A., Bar-Shalom, Y., Dayan, J., 1998. Interacting multiple model methods in target tracking: a survey. IEEE Trans. on Aeros. Electron. Syst., 34(1):103-123.

[16] McGinnity, S., Irwin, G.W., 2000. Multiple model bootstrap filter for maneuvering target tracking. IEEE Trans. on Aeros. Electron. Syst., 36(3):1006-1012.

[17] Musso, C., Oudjane, N., Legland, F., 2001. Improving Regularized Particle Filters. In: Doucet, A., de Freitas, J.F.G., Gordon, N.J. (Eds.), Sequential Monte Carlo Methods in Practice. Springer-Verlag, New York, p.247-272.

[18] Press, W.H., Teukolsky, S.A., Vetterling, W.T., Flannery, B.P., 1992. Numerical Recipes in C: The Art of Scientific Computing (2nd Ed.). Cambridge University Press.

[19] Robert, C.P., Casella, G., 1999. Monte Carlo Statistical Method. Springer-Verlag, New York.

[20] van der Merwe, R., Doucet, A., de Freitas, N., Wan, E., 2000. The Unscented Particle Filter. Technical Report, CUED/ FINFENG/TR380. Engineering Department, Cambridge University.

[21] van der Merwe, R., 2004. Sigma-point Kalman Filters for Probabilistic Inference in Dynamic State-space Models. Ph.D Thesis, Oregon Health Sci. Univ., Portland, OR.

[22] Wan, E.A., van der Merwe, R., 2000. The Unscented Kalman Filter for Nonlinear Estimation. Proc. Symp. on Adaptive Systems for Signal Processing, Communication and Control, p.153-158.