Resampling from the Niching Genetic Algorithm Applicated in Extended Kalman Particle Filter  

作　　者：QIN Honglei LI Ziyu CONG Li 

机构地区：[1]School of Electronic and Information Engineering, Beihang University, Beijing 100191, China [2]Beijing Aerospace Control Center, Beijing 102206, China

出　　处：《Chinese Journal of Electronics》2011年第3期553-559,共7页电子学报（英文版）

摘　　要：Two serious problems existing in Particle filter （PF） are the degeneracy phenomenon and the sam- ple impoverishment caused by simple random resampling. In this paper, based on the Extended Kalman particle fil- ter （EKPF） which selects the importance distribution of PF by the Extended Kalman filter （EKF）, we propose a new resampling method from niching genetic algorithm to inhibit the degeneracy phenomenon and avoid the sample impoverishment problem, and name the improved particle filtering algorithm as Niching genetic algorithm Extended Kalman particle filter （NGA-EKPF）. According to the theoretical analysis and computer simulation of three algorithms in the Global positioning system （GPS）, i.e. EKF, EKPF with simple random resampling and NGA-EKPF, the performance of the proposed algorithm has improvement compared with other algorithms not only in positioning accuracy, but also by Cramer-Rao low bound （CRLB） which provides a theoretical bound on the filtering performance.

关 键 词：Extended Kalman particle filter （EKPF） RESAMPLING Niching genetic algorithm （NGA） CramerRao lower bound （CRLB）. 

分 类 号：TP242[自动化与计算机技术—检测技术与自动化装置] N941.5[自动化与计算机技术—控制科学与工程]