TY - GEN
T1 - A mixed fast particle filter
AU - Fasheng, Wang
AU - Qingjie, Zhao
AU - Hongbin, Deng
PY - 2007
Y1 - 2007
N2 - Particle filtering algorithm has been widely used in solving nonlinear/non-Gaussian filtering problems. In this paper, a new particle filter is proposed, which is based on the unscented Kalman filter (UKF) and the extended Kalman filter (EKF), and takes a divide-and-conquer sampling strategy. It first uses a mixed Kaiman filter, which combines UKF and EKF, as proposal distribution to generate part of the particles, and then uses the transition prior for another part. The experiment results show that this new particle filter can reduce time cost in addition to giving higher accuracy compared to other particle filters.
AB - Particle filtering algorithm has been widely used in solving nonlinear/non-Gaussian filtering problems. In this paper, a new particle filter is proposed, which is based on the unscented Kalman filter (UKF) and the extended Kalman filter (EKF), and takes a divide-and-conquer sampling strategy. It first uses a mixed Kaiman filter, which combines UKF and EKF, as proposal distribution to generate part of the particles, and then uses the transition prior for another part. The experiment results show that this new particle filter can reduce time cost in addition to giving higher accuracy compared to other particle filters.
UR - https://www.scopus.com/pages/publications/35148847791
U2 - 10.1109/SNPD.2007.42
DO - 10.1109/SNPD.2007.42
M3 - Conference contribution
AN - SCOPUS:35148847791
SN - 0769529097
SN - 9780769529097
T3 - Proceedings - SNPD 2007: Eighth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing
SP - 932
EP - 936
BT - Proceedings - SNPD 2007
T2 - SNPD 2007: 8th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing
Y2 - 30 July 2007 through 1 August 2007
ER -