In this paper, a novel particle filter based on one-step smoothing is proposed for nonlinear systems with random one-step delay and missing measurements. Such problems are commonly encountered in networked control systems, where random one-step delay and missing measurements significantly increase the difficulty of dynamic state estimation. The particle filter is a nonlinear filtering method based on sequential Monte Carlo sampling. It shows excellent state estimation performance when dealing with nonlinear and non-Gaussian dynamic systems. However, the particle filter has certain limitations in complex dynamic scenarios, with particle degradation being the most typical issue, which can significantly reduce estimation accuracy. To address particle degradation, the proposed particle filter iteratively incorporates current measurement information derived from sensors into the prior distribution to construct a new importance function. This approach can limit particle degeneracy and improve the efficiency of importance sampling in the bootstrap particle filter. Simulation experiments demonstrate that the proposed particle filter effectively limits particle degradation and improves estimation accuracy compared to the existing bootstrap particle filter for nonlinear systems with random one-step delay and missing measurements.
Keywords: importance function; missing measurements; nonlinear filter; one-step delay; one-step smoothing; particle filter.