The structure of particle filter for multi-sensor information fusion system is the key to expanding the application domain of sampling nonlinear filters.Aiming at the effective realization of particle filter in multi-sensor fusion tracking system,a novel multi-sensor fusion target tracking algorithm based on Rao-Blackwellised particle filter is proposed.In the new algorithm,the reduction of tracking system state dimension is firstly realized by the Rao-Blackwellised modeling technology.Secondly,combining with the characteristics of multi-sensor fusion system,a new weight optimization method is used to improve the reliability and stability of particle weight.Finally,the system nonlinear and linear state components are respectively estimated by particle filter and Kalman filter,and the system state estimation is achieved by the state reconstruction method at the current time.The theoretical analysis and experimental results show the feasibility and efficiency of the proposed algorithm.