Particle Filters for Random Set Models

This book discusses state estimation of shastic dynamic systems from noisy measurements, specifically sequential Bayesian estimation and nonlinear or shastic filtering. The class of solutions presented in this book is based on the Monte Carlo statistical method. Although the resulting algorithms, known as particle filters, have been around for more than a decade, the recent theoretical developments of sequential Bayesian estimation in the framework of random set theory have provided new opp...