Particle filter based on one‐step smoothing with adaptive iteration
Zhibin Yan, Yanhua Yuan · IET Signal Processing · 2017
A new one‐step particle smoother is explicitly given in the form of proper weighted samples. It is employed iteratively to improve the importance sampling in particle filtering through incorporating the current measurement information into the a priori distribution. An adaptive iteration strategy is proposed to accelerate the running, which introduces a parameter into the weight increment to adjust the iteration process. Then, new particle filtering method can be constructed through combining the one‐step smoothing and the adaptive iteration strategy.