Particle filter for nonlinear systems with multiple step randomly delayed measurements
A new particle filter for nonlinear systems with multiple step randomly delayed measurements is proposed. In the proposed method, particles and their weights are updated in the Bayesian estimation framework by considering the multiple step randomly delayed measurement model. Simulation results show that the proposed method has higher estimation accuracy than existing methods.