Multi-sensor marginalized particle filter based on average weight optimization in correlated noise

Abstract Particle filter is a kind of powerful and effective simulation-based method to perform optimal state estimation in nonlinear non-Gaussian state-space models. However, its main drawback is with large computational complexity and not suitable for noise correlation condition, which limits its application in the multi-sensor measurement system. Aiming at the above problem, a novel multi-sensor marginalized particle filter based on average weight optimization in correlated noise is proposed. First, marginalized particle filter is used as the basic framework of new algorithm realization by marginalizing the states appearing linearly in the dynamical system, and the objective is to reduce the calculated amount. Second, considering the rational utilization of multi-sensor measurement, the average weight optimization strategy is used to improve the adverse influence caused by random measurement noise in measuring process of particles weight. Third, combining with the model reconstruction technology, a new decoupling approach of correlated noise is designed in multi-sensor measurement. Finally, the theoretical analysis and experimental results show the feasibility and efficiency of the proposed algorithm.