An Adaptive Optimized Strategy for Particle Filter
Particle filter has been widely applied into many fields in recent years. Combined with the deficiency analysis of particle filter, an adaptive optimized strategy for particle filter is proposed. This adaptive optimized strategy includes two parts. One is an improved hybrid proposal distribution with adaptive parameter optimization for particle filter. Based on the performance analysis of different proposal distribution, a hybrid proposal distribution with adaptive annealing parameter optimization is utilized to consider current information of the latest observed measurement. The other is an adaptive resampling strategy based on diversity guidance. An adaptive resampling step in particle filter is tuned based on two diversity measures and an improved partial stratified resampling strategy is presented based on the weights optimal idea. With the simulation program, the performance of the proposed strategy is evaluated and its validity is verified.
Particle filter Hybrid proposal distribution Adaptive parameter optimization Adaptive resampling Diversity measure Improved partial stratified resampling
Yu Jinxia Tang Yongli Xu Jingmin Zhao Qian
College of Computer Science and Technology, Henan Polytechnic University, Jiaozuo 454003, China Key State Grid Electric Power of HeNan Ji-Yuan Power Supply Company, Jiyuan 454650, China
国际会议
The 24th Chinese Control and Decision Conference (第24届中国控制与决策学术年会 2012 CCDC)
太原
英文
3953-3957
2012-05-23（万方平台首次上网日期，不代表论文的发表时间）