TY - GEN
T1 - Consensus-based particle filter
AU - Liu, Xiangyu
AU - Wang, Yan
PY - 2012
Y1 - 2012
N2 - The particle filter is well known as a state estimation method for nonlinear and non-Gaussian system. However, particle filter has the inherent drawbacks such as samples less of diversity and low tracking accuracy. In this paper, a novel particle filter algorithm with the Markov Chain Monte Carlo (MCMC) and consensus strategy is proposed. The authors utilize MCMC sampling method to make the particles more diversification. And the algorithm is optimized by consensus strategy, which makes the state estimates of all network nodes converge to a more precise value. Simulation results show that compared to existing methods, the proposed algorithm has superior performance.
AB - The particle filter is well known as a state estimation method for nonlinear and non-Gaussian system. However, particle filter has the inherent drawbacks such as samples less of diversity and low tracking accuracy. In this paper, a novel particle filter algorithm with the Markov Chain Monte Carlo (MCMC) and consensus strategy is proposed. The authors utilize MCMC sampling method to make the particles more diversification. And the algorithm is optimized by consensus strategy, which makes the state estimates of all network nodes converge to a more precise value. Simulation results show that compared to existing methods, the proposed algorithm has superior performance.
KW - Consensus
KW - Markov Chain Monte Carlo
KW - Particle Filter
KW - Sample Impoverishment
UR - https://www.scopus.com/pages/publications/84874306263
U2 - 10.1109/ICCECT.2012.158
DO - 10.1109/ICCECT.2012.158
M3 - 会议稿件
AN - SCOPUS:84874306263
SN - 9780769548814
T3 - Proceedings - 2012 International Conference on Control Engineering and Communication Technology, ICCECT 2012
SP - 577
EP - 580
BT - Proceedings - 2012 International Conference on Control Engineering and Communication Technology, ICCECT 2012
T2 - 2012 International Conference on Control Engineering and Communication Technology, ICCECT 2012
Y2 - 7 December 2012 through 9 December 2012
ER -