Particle filter is a computer-based method for implementing an optimal recursive Bayesian filter by Monte Carlo simulations.The method may cope with any nonlinear model without any limitations of linearization error and Gaussian noises assumption

so it can be used for the state estimation problem of non-Gaussian nonlinear systems.In order to solve the centralized multisensor sate estimation problem of non-Gaussian nonlinear system

the paper proposes a new multisensor sequential particle filter.First

the general theoretical model of centralized multisensor particle filter is got.Then

a sequential resampling method is proposed according to the characteristics of centralized multisensor system.At last

a Monte Carlo simulation is used to analyze the performance of the method.The results of the simulation show that the new method can greatly improve the state estimation precision of multisensor system.Moreover

it will get more accurate estimation with more sensors.