Chapter 8 Markov Chain Monte Carlo Particle Algorithms for Discrete-Time Nonlinear Filtering

摘要

This work shows how a carefully designed instrumental distribution can improve the performance of a Markov chain Monte Carlo (MCMC) particle filter for systems with a high state dimension (up to 100). We devise a special subgradient-based kernel from which candidate moves are drawn. This facilitates the implementation of the filtering algorithm in high-dimensional settings using a remarkably small number of particles. We demonstrate our approach in solving a nonlinear non-Gaussian dynamic compressed sensing (l_1 constrained) problem and show high estimation accuracy.