Two-stage prediction and update particle filtering algorithm based on particle weight optimization in multi-sensor observation

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.