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Particle Filters for Random Set Models
Branko Ristic
€ 182.69
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Description for Particle Filters for Random Set Models
Hardback. This book covers state estimation of stochastic dynamic systems from noisy measurements, specifically sequential Bayesian estimation and nonlinear or stochastic filtering. Describes applications in multi-target systems, video tracking of pedestrians and more. Num Pages: 174 pages, 5 black & white tables, biography. BIC Classification: UYAM. Category: (P) Professional & Vocational. Dimension: 235 x 155 x 18. Weight in Grams: 450.
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.
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.
Product Details
Format
Hardback
Publication date
2013
Publisher
Springer-Verlag New York Inc. United States
Number of pages
174
Condition
New
Number of Pages
174
Place of Publication
New York, NY, United States
ISBN
9781461463153
SKU
V9781461463153
Shipping Time
Usually ships in 15 to 20 working days
Ref
99-15
About Branko Ristic
Branko Ristic is at the Defence Science and Technology Organisation, Australia Defence Science and Technology Organisation, Australia
Reviews for Particle Filters for Random Set Models
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)