@inproceedings{31d0739563d4414e93a2333cb0688e8c,
title = "Application of proximal support vector regression to particle filter",
abstract = "An improved particle filter for nonlinear, non-Gaussian estimation is proposed in this paper. The algorithm consists of a particle filter that uses a proximal support vector regression (PSVR) based re-weighting scheme to re-approximate the posterior density and avoid sample impoverishment. A regression function is obtained by PSVR over the weighted sample set and each sample is re-weighted via this function. Then, posterior density of the state is re-approximated to maintain the effectiveness and diversity of samples. Two experimental results demonstrate that the efficiency of the proposed algorithm compared with the generic particle filter and Markov Chain Monte Carlo (MCMC) particle filter.",
keywords = "Particle filter, Proximal support vector regression, Support vector machine",
author = "Wei Jiang and Guoxing Yi and Qingshuang Zeng",
year = "2009",
doi = "10.1109/ICICISYS.2009.5357867",
language = "英语",
isbn = "9781424447541",
series = "Proceedings - 2009 IEEE International Conference on Intelligent Computing and Intelligent Systems, ICIS 2009",
pages = "239--243",
booktitle = "Proceedings - 2009 IEEE International Conference on Intelligent Computing and Intelligent Systems, ICIS 2009",
note = "2009 IEEE International Conference on Intelligent Computing and Intelligent Systems, ICIS 2009 ; Conference date: 20-11-2009 Through 22-11-2009",
}