TY - GEN
T1 - Improved Particle Filter Based on Kernel Density Estimation
AU - Huang, Pan
AU - Qiang, Xingzi
AU - Xue, Rui
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021/5/7
Y1 - 2021/5/7
N2 - Particle filter (PF) algorithm can be used for nonlinear and non-gaussian system state estimation, the core lies in how to accurately describe the posterior density function with limited particles. In recent years, there have been many improved particle filter algorithms, but the effect is not good in the strong nonlinear/non-Gaussian environment. The improved method proposed in this paper can overcome this difficulty. This method uses kernel density estimation to fit the priori PDF, then evenly sample new particles in a reasonable area and calculate their priori weights. Then these particles' weights are updated according to the likelihood function, and then normalize the weights, and the state is estimated by using the particles and their normalized weights. This method is called KDE-PF. Simulation results show that this method is better than the general particle filter algorithm.
AB - Particle filter (PF) algorithm can be used for nonlinear and non-gaussian system state estimation, the core lies in how to accurately describe the posterior density function with limited particles. In recent years, there have been many improved particle filter algorithms, but the effect is not good in the strong nonlinear/non-Gaussian environment. The improved method proposed in this paper can overcome this difficulty. This method uses kernel density estimation to fit the priori PDF, then evenly sample new particles in a reasonable area and calculate their priori weights. Then these particles' weights are updated according to the likelihood function, and then normalize the weights, and the state is estimated by using the particles and their normalized weights. This method is called KDE-PF. Simulation results show that this method is better than the general particle filter algorithm.
KW - kernel density estimation
KW - nonlinear/non-Gaussian environment
KW - particle filtering
KW - state estimation
UR - https://www.scopus.com/pages/publications/85112828588
U2 - 10.1109/ICET51757.2021.9451139
DO - 10.1109/ICET51757.2021.9451139
M3 - 会议稿件
AN - SCOPUS:85112828588
T3 - 2021 IEEE 4th International Conference on Electronics Technology, ICET 2021
SP - 1015
EP - 1019
BT - 2021 IEEE 4th International Conference on Electronics Technology, ICET 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 4th IEEE International Conference on Electronics Technology, ICET 2021
Y2 - 7 May 2021 through 10 May 2021
ER -