Particle Filters for Random Set Models
This book discusses 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. 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 opportunities which are not widely known and are covered in this book. This book is ideal for graduate students, researchers, scientists and engineers interested in Bayesian estimation.
Presents a hands-on engineering approach to filtering algorithms and their implementation Covers a new generation of particle filters, which are applicable to a much wider class of signal processing applications Includes sensor control for particle filters Provides information on a number of interesting and relevant applications, which illustrate theoretical concepts and demonstrate the performance of developed particle filters