Particle Filters for Random Set Models

Particle Filters for Information Fusion Using Random Sets presents coverage of state estimation of stochastic dynamic systems from noisy measurements, specifically sequential Bayesian estimation and nonlinear or stochastic filtering. The class of solutions presented in this book is based on the Monte Carlo statistical method. The resulting algorithms, known as particle filters, in the last decade have become one of the essential tools for stochastic filtering, with applications ranging from navigation and autonomous vehicles to bio-informatics and finance. §While 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 opportunities which are not widely known and are covered in this monograph. These recent developments have dramatically widened the scope of applications, from single to multiple appearing/disappearing objects, from precise to imprecise measurements and measurement models. §Coverage of how these filtering algorithms can be implemented using the sequential Monte Carlo method as particle filters is provided. Additionally, several applications of this new class of particle filters will be presented. Some of the applications included in the book are: Calibration of multi-target systems; Prediction of the spread of an epidemic; Processing natural language statements; and Video tracking of pedestrians. §This book is ideal for graduate students, researchers, scientists and engineers interested in Bayesian estimation.