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.

From the book reviews:

“The book realizes a happy union between theory and practice. Of high interest are the Algorithms for which their pseudo-codes are presented. We think we are faced with an excellent book that will have a great success and audience between those interested for new approaches in filtering theory.” (Dumitru Stanomir, zbMATH 1306.93002, 2015)

Branko Ristic is at the Defence Science and Technology Organisation, Australia

Defence Science and Technology Organisation, Australia