A particle-inspired Monte Carlo tree estimation method in Bayesian filtering

摘要

A particle-inspired Monte Carlo tree estimation method is proposed to avoid repeating similar simulation and handle the depletion problem in particle filter. Under the inspiration of particles, the method divides the state-space recursively in a top-down manner to form a tree structure that each node in the tree is corresponding to a sub-space. Particles are allocated to the corresponding terminal node during the procedure. Certain size of minimal sub-space or piece is specified to terminate the dividing. Each piece is corresponding to a leaf-node of the tree structure and the prediction probability density in it is approximated by the proportion of its particles in total particles. Instead of importance sampling for each particle, the method takes uniformly random measurements to compute the posterior probability density in each piece. As a result, the method is applied to growth model and has better performance in high SNR enviromnents compared with the Sampling Importance Resampling method.