The reasonable measuring of particle weight and effective sampling of particle state are considered as two important aspects to obtain better estimation precision in particle filter.Aiming at the comprehensive treatme...The reasonable measuring of particle weight and effective sampling of particle state are considered as two important aspects to obtain better estimation precision in particle filter.Aiming at the comprehensive treatment of above problems,a novel two-stage prediction and update particle filtering algorithm based on particle weight optimization in multi-sensor observation is proposed.Firstly,combined with the construction of multi-senor observation likelihood function and the weight fusion principle,a new particle weight optimization strategy in multi-sensor observation is presented,and the reliability and stability of particle weight are improved by decreasing weight variance.In addition,according to the prediction and update mechanism of particle filter and unscented Kalman filter,a new realization of particle filter with two-stage prediction and update is given.The filter gain containing the latest observation information is used to directly optimize state estimation in the framework,which avoids a large calculation amount and the lack of universality in proposal distribution optimization way.The theoretical analysis and experimental results show the feasibility and efficiency of the proposed algorithm.展开更多
基金National Natural Science Foundation of China under Grant No.10674025the Funds from Key Lab of Quantum Information,University of Science and Technology of China,and the Development Funds of Fuzhou University under Grant No.2007-XY-15
基金Supported by the National Natural Science Foundations of China(No.61300214,61170243)the Science and Technology Innovation Team Support Plan of Education Department of Henan Province(No.13IRTSTHN021)+2 种基金the Science and Technology Research Key Project of Education Department of Henan Province(No.13A413066)the Basic and Frontier Technology Research Plan of Henan Province(No.132300410148)the Funding Scheme of Young Key Teacher of Henan Province Universities,and the Key Project of Teaching Reform Research of Henan University(No.HDXJJG2013-07)
文摘The reasonable measuring of particle weight and effective sampling of particle state are considered as two important aspects to obtain better estimation precision in particle filter.Aiming at the comprehensive treatment of above problems,a novel two-stage prediction and update particle filtering algorithm based on particle weight optimization in multi-sensor observation is proposed.Firstly,combined with the construction of multi-senor observation likelihood function and the weight fusion principle,a new particle weight optimization strategy in multi-sensor observation is presented,and the reliability and stability of particle weight are improved by decreasing weight variance.In addition,according to the prediction and update mechanism of particle filter and unscented Kalman filter,a new realization of particle filter with two-stage prediction and update is given.The filter gain containing the latest observation information is used to directly optimize state estimation in the framework,which avoids a large calculation amount and the lack of universality in proposal distribution optimization way.The theoretical analysis and experimental results show the feasibility and efficiency of the proposed algorithm.
