Nonlinear filtering with multiple packet dropouts

This paper considers the nonlinear system filtering with packet dropouts. We assume that the packet arrived rate is known in advance but the sequence of packet dropouts is unknown. At first, we use the probability-weighted method to achieve a pseudo measurement sequence, and every pseudo measurement is the weighted value of the measurements acquired at the current time step and the prior time step. Some classical nonlinear filtering methods can be used via the pseudo measurement sequence and the dynamic equation of the system, and then the pseudo measurement unscented Kalman filter (PM_UKF) and the pseudo measurement particle filter (PM_PF) are given. This pseudo measurement sequence can be also used in the linear system, and its time complexity is lower than that of Sun's optimal filter at this time. Simulation results show the effectiveness of the proposed algorithms.