메뉴 건너뛰기




Volumn 15, Issue PART 1, 2009, Pages 1358-1363

A survey of sample size adaptation techniques for particle filters

Author keywords

Adaptation; Particle filter; Sample size; State estimation

Indexed keywords

ADAPTATION; ADAPTATION TECHNIQUES; DISCRETE-TIME; KEEPING QUALITY; KEY PARAMETERS; NON-LINEAR NON-GAUSSIAN; PARTICLE FILTER; SAMPLE SIZES;

EID: 80051635048     PISSN: 14746670     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.3182/20090706-3-FR-2004.0198     Document Type: Conference Paper
Times cited : (21)

References (19)
  • 6
    • 0344445520 scopus 로고    scopus 로고
    • Adapting the sample size in particle filters through kldsampling
    • D. Fox. Adapting the sample size in particle filters through kldsampling. International Journal of Robotics Research, 22: 985-1003, 2003.
    • (2003) International Journal of Robotics Research , vol.22 , pp. 985-1003
    • Fox, D.1
  • 7
    • 0001667705 scopus 로고
    • Bayesian inference in econometric models using monte carlo integration
    • J. Geweke. Bayesian inference in econometric models using monte carlo integration. Econometrika, 24:1317-1399, 1989.
    • (1989) Econometrika , vol.24 , pp. 1317-1399
    • Geweke, J.1
  • 8
    • 0027580559 scopus 로고
    • Novel approach to nonlinear/ non-gaussian bayesian state estimation
    • N. Gordon, D. Salmond, and A. F. M. Smith. Novel approach to nonlinear/ non-gaussian bayesian state estimation. IEE Proceedings-F, 140:107-113, 1993.
    • (1993) IEE Proceedings-F , vol.140 , pp. 107-113
    • Gordon, N.1    Salmond, D.2    Smith, A.F.M.3
  • 9
    • 30844457970 scopus 로고    scopus 로고
    • Monte carlo data association for multiple target tracking
    • Eindhoven, NL
    • R. Karlsson and F. Gustafsson. Monte carlo data association for multiple target tracking. In IEE Workshop on Target Tracking, Eindhoven, NL, 2001.
    • (2001) IEE Workshop on Target Tracking
    • Karlsson, R.1    Gustafsson, F.2
  • 11
    • 0003987291 scopus 로고    scopus 로고
    • Using learning for approximation in stochastic processes
    • Morgan Kaufmann, San Francisco, CA
    • D. Koller and R. Fratkina. Using learning for approximation in stochastic processes. In Proc. 15th International Conf. on Machine Learning, pages 287-295. Morgan Kaufmann, San Francisco, CA, 1998.
    • (1998) Proc. 15th International Conf. on Machine Learning , pp. 287-295
    • Koller, D.1    Fratkina, R.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.