Aiming to adverse influence on the filtering precision of nonlinear state estimation caused by the random observation noise and the improvement of larger calculated amount from linear state estimation in marginalized particle filter

a novel real-time marginalized particle filter based on weights consistency optimization is proposed.Firstly

according to the extraction and utilization of prior information from observation system model

the consistency optimization method of particle weights in observation lifting scheme is given by the construction of consistency distance and consistency matrix

which improves the filtering precision of particle filter used in nonlinear state estimation.Secondly

the real-time marginalized particle filter is proposed by the structure optimization of time update and observation update steps

which decrease the computational complexity of Kalman filter used in the linear state estimation in view of Monte Carlo simulation principle.Finally

the concrete steps of new algorithm are given by the dynamic combination of the consistency optimization method and the real-time marginalized particle filter.The filtering precision and calculated amount of new algorithm is analyzed on the basis of single station radar observation target tracking simulation scene.The theoretical analysis and experimental results show the feasibility and efficiency of algorithm proposed.