Enhancing particle filters using local likelihood sampling

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 3021, Issue , 2004, Pages 16-27

  Torma, Péter ^a   Szepesvári, Csaba ^b  

^a Tateyama Laboratory Hungary Ltd   (Hungary)

^b INSTITUTE OF EXPERIMENTAL MEDICINE   (Hungary)

Author keywords

[No Author keywords available]

Indexed keywords

MONTE CARLO METHODS; TRACKING (POSITION);

LOCAL LIKELIHOOD; NON LINEAR; OBSERVATION NOISE; PARTICLE FILTER; REAL-WORLD; SYSTEM NOISE; TWO STAGE SAMPLINGS; VISUAL TRACKING;

COMPUTER VISION;

EID: 33749043378     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-24670-1_2     Document Type: Article

References (8)

1. Pitt, M.K., Shephard, N.: Filtering via simulation: Auxiliary particle filter. Journal of the American Statistical Association 94 (1999) 590-599
2. Fox, D., Thrun, S., Burgard, W., Dellaert, F.: Particle-filters for mobile robot localization. In Doucet, A., de Freitas, N., Gordon, N., eds.: Sequential Monte Carlo Methods in Practice, New York, Springer (2001)
3. Torma, P., Szepesvári, C.: LS-N-IPS: an improvement of particle filters by means of local search. Proc. Non-Linear Control Systems(NOLCOS'0l) St. Petersburg, Russia (2001)
4. Liu, J.S., Chen, R.: Sequential Monte Carlo methods for dynamic systems. Journal of the American Statistical Association 93 (1998) 1032-1044
5. Liu, J.S.: Metropolized independent sampling. Statistics and Computing 6 (1996) 113-119
6. Kong, A.: A note on importance sampling using renormalized weights. Technical report (1992)
7. Rubin, D.: Using the SIR algorithm to simulate posterior distributions. In J.M. Bernardo, M.H. DeGroot, D.L.e.A.S., ed.: Bayesian Statistics 3. Oxford University Press (1988) 395-402
8. Isard, M., Blake, A.: CONDENSATION - conditional density propagation for visual tracking. International Journal Computer Vision 29 (1998) 5-28