TY  - BOOK
AU  - Ristic,Branko
TI  - Particle filters for random set models
SN  - 9781461463153
U1  - 519.2 23
PY  - 2013///
CY  - New York
PB  - Springer
KW  - Random sets
KW  - Stochastic processes
KW  - Estimation theory
N1  - Includes bibliographical references and index; 1. Introduction --
2. Background --
3. Applications Involving Non-standard Measurements --
4. Multi-Object Particle Filters --
5. Sensor Control for Random Set BasedParticle Filters --
6. Multi-Target Tracking --
7. Advanced Topics--

References--
Index.


N2  - 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
ER  -