Modified unscented particle filter for nonlinear Bayesian tracking

Journal of Systems Engineering and Electronics

Volumn 19, Issue 1, 2008, Pages 7-14

  Ronghui, Zhan ^a   Qin, Xin ^a   Jianwei, Wan ^a  

^a NATIONAL UNIVERSITY OF DEFENSE TECHNOLOGY   (China)

Author keywords

Bayesian estimation; modified unscented particle filter; nonlinear filtering; unscented Kalman filter

Indexed keywords

BAYESIAN ESTIMATION; MODIFIED UNSCENTED PARTICLE FILTERS; UNSCENTED KALMAN FILTERS;

COMPUTER SIMULATION; KALMAN FILTERS; NONLINEAR FILTERING;

BAYESIAN NETWORKS;

EID: 39649122365     PISSN: 10044132     EISSN: None     Source Type: Journal    
DOI: 10.1016/S1004-4132(08)60038-9     Document Type: Article

References (11)

1. Bar-Shalom Y., Li X., and Kirubarajan T. Estimation with application to tracking and navigation: theory, algorithm, and software (2001), Wiley, New York
2. Julier J., and Uhlmann K. A new method for the nonlinear transformation of means and covariances in filters and estimators. IEEE Trans. Autom. Control 45 3 (2000) 477-482
3. Gordon N., Salmond D., and Smith A. Novel approach to nonlinear/non-Gaussian Bayesian state estimation. Proc. Inst.Elect.Eng.F 140 2 (1993) 107-113
4. Arulampalam M., Maskell S., Gordon N., et al. A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking. IEEE Trans. Signal Process. 50 2 (2002) 174-188
5. Doucet A., Freitas d., and Gordon N. Sequential montecarlo methods in practice (2001), Springer-Verlag, New York
6. Zhe C, Thia Kirubarajan, Morelande Mark R. Improved particle filtering schemes for target tracking. in Proc. Int. Conf. Acoust., Speech, Signal Process., Philadelphia, PA, 2005: IV145-148.
7. Merwe van der R, Freitas de N, Doucet A, et al. The unscented particle filter cambridge univ. Eng. Dept., Tech. Rep.CUED/F-INFENG/TR-380, 2000.
8. Rui Y, Chen Y. Better proposal distributions: object tracking using unscented particle filter. Proc. IEEE Conf. on. Computer Vision and Pattern Recognition, Kauai, Hawaii, 2001: 786-793.
9. Merwe van der R. Sigma-point Kalman filters for probabilistic inference in dynamic state-space models. Electrical and Computer Engineering Ph.D. Dissertation, Oregon Health Sciences Univ., Portland, OR, 2004.
10. Merwe van der R, Wan E A. The square-root unscented Kalman filter for state and parameter-estimation. in Proc. Int. Conf. Acoust., Speech, Signal Process., Salt Lake City, UT, 2001: 3461-3464.
11. Wu Y., Hu D., Wu M., et al. Unscented Kalman filtering for additive noise case: augmented versus nonaugmented. IEEE Signal Process. Letters 12 5 (2005) 357-360