[1]LIU Feng,XUAN Shibin,LIU Xiangpin.Quasi-Monte Carlo particle filter algorithm based on the good point set and its application[J].CAAI Transactions on Intelligent Systems,2014,9(4):461-467.[doi:10.3969/j.issn.1673-4785.201305079]
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Quasi-Monte Carlo particle filter algorithm based on the good point set and its application

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 9 Number of periods: 2014 4 Page number: 461-467 Column: 学术论文—智能系统 Public date: 2014-08-25
Title:
Quasi-Monte Carlo particle filter algorithm based on the good point set and its application
Author(s):
LIU Feng1;  XUAN Shibin2;  LIU Xiangpin1
1. College of Information Science and Engineering, Guangxi University for Nationalities, Nanning 530006, China;
2. Guangxi Key Laboratory of Hybrid Computation and IC Design Analysis, Nanning 530006, China
Keywords:
target tracking; particle filter algorithm; quasi-Monte Carlo; good point set; occlusion
CLC:
TP391.9
DOI:
10.3969/j.issn.1673-4785.201305079
Abstract:
A quasi-Monte Carlo particle filtering algorithm based on the good point set (GPS-QMCPF) is proposed for solving the problem of particle shortage and sample aggregation. In the proposed algorithm, a new quasi-Monte Carlo sequence is constructed by using the good point set theory in the number theory. Considering that the good point set has a more homogeneous distribution and lower discrepancy than the standard QMC sequence and the random sequence, GPS-QMCPF can obtain a faster convergence speed in the filtering process and a better accuracy of the state estimation. Furthermore the re-sampling frequency is reduced, which results in a lower computational cost. Experimental results show that the proposed algorithm gets a more accurate estimation than the standard QMC filter and particle filter in the system state estimation, as well as with the video target tracking application. The proposed algorithm possesses the advantages of good tracking accuracy and a real-time standard, even in the case of occlusions.
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