Efficient nonlinear measurement updating based on gaussian mixture approximation of conditional densities

Proceedings of the American Control Conference

Volumn , Issue , 2007, Pages 4425-4430

  Huber, Marco F ^a   Brunn, Dietrich ^a   Hanebeck, Uwe D ^a  

^a UNIVERSITY OF KARLSRUHE   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

APPROXIMATION THEORY; CLUSTER ANALYSIS; DYNAMIC PROGRAMMING; FILTRATION; GAUSSIAN DISTRIBUTION; MIXTURES; NONLINEAR SYSTEMS; POLYNOMIAL APPROXIMATION; RANDOM VARIABLES; TRELLIS CODES;

(P ,P ,T) MEASUREMENTS; APPROXIMATE CALCULATIONS; CLOSED FORM; CONDITIONAL DENSITIES; EXACT SOLUTIONS; EXPONENTIAL GROWTH; GAUSSIAN MIXTURE (GM); GENERAL (CO); HIGH QUALITY (HQ); NONLINEAR MEASUREMENTS; NONLINEAR STOCHASTIC DYNAMIC SYSTEMS; OPTIMIZ ATION PROBLEMS; PROBABILISTIC REPRESENTATIONS; PROGRESSIVE PROCESSING; SYSTEM STATES;

MEASUREMENTS;

EID: 46449107682     PISSN: 07431619     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ACC.2007.4282269     Document Type: Conference Paper

References (13)

1. D. L. Alspach and H. W. Sorenson, "Nonlinear Bayesian Estimation using Gaussian Sum Approximation," IEEE Transactions on Automatic Control, vol. 17, no. 4, pp. 439-448. August 1972.
2. S. Arulampalam, S. Maskell, N. Gordon, and T. Clapp, "A Tutorial on Particle Filters for Online Nonlinear/Non-Gaussian Bayesian Tracking," IEEE Transactions of Signal Processing, vol. 50, no. 2, pp. 174-188, 2002.
3. R. Fletcher, Practical Methods of Optimization, 2nd ed. John Wiley and Sons Ltd. 2000.
4. U. D. Hanebeck, K. Briechle, and A. Rauh, "Progressive Bayes: A New Framework for Nonlinear State Estimation," in Proceedings of SPIE, vol. 5099. AeroSense Symposium, 2003, pp. 256-267.
5. S. Haykin, Communication Systems, 4th ed. John Wiley & Sons, Inc., 2001.
6. M. Huber, D. Brunn, and U. D. Hanebeck, "Closed-Form Prediction of Nonlinear Dynamic Systems by Means of Gaussian Mixture Approximation of the Transition Density," in IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, 2006, pp. 98-103.
7. S. J. Julier and J. K. Uhlmann, "Unscented Filtering and Nonlinear Estimation," in Proceedings of the IEEE, vol. 92, no. 3, 2004, pp. 401-422.
8. R. E. Kalman, "A new Approach to Linear Filtering and Prediction Problems," Transactions of the ASME, Journal of Basic Engineering, no. 82, pp. 35-45, 1960.
9. P. S. Maybeck and B. D. Smith, "Multiple Model Tracker Based on Gaussian Mixture Reduction for Maneuvering Targets in Clutter," in 8th International Conference on Information Fusion, vol. 1, 2005, pp. 40-47.
10. V. Maz'ya and G. Schmidt, "On Approximate Approximations using Gaussian Kernels," IMA Journal of Numerical Analysis, vol. 16, no. 1, pp. 13-29, 1996.
11. N. Oudjane and C. Musso, "Progressive Correction for Regularized Particle Filters," in Proceedings of the 3rd International Conference on Information Fusion, 2000, pp. THB2/10-THB2/17.
12. A. Papoulis, Probability, Random Variables and Stochastic Processes, 3rd ed. McGraw-Hill, 1991.
13. D. J. Salmond, "Mixture Reduction Algorithms for Target Tracking in Clutter," in SPIE Signal and Data Processing of Small Targets, ser. 1305, April 1990, pp. 434-445.