| 書目名稱 | Particle Filters for Random Set Models | | 編輯 | Branko Ristic | | 視頻video | http://file.papertrans.cn/742/741634/741634.mp4 | | 概述 | 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 a | | 圖書封面 |  | | 描述 | 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. | | 出版日期 | Book 2013 | | 關(guān)鍵詞 | Bayesian Estimation; Bernoulli Filter; Filtering Algorithms; Monte Carlo Statistical Method; Multi-targe | | 版次 | 1 | | doi | https://doi.org/10.1007/978-1-4614-6316-0 | | isbn_softcover | 978-1-4899-8884-3 | | isbn_ebook | 978-1-4614-6316-0 | | copyright | Springer Science+Business Media New York 2013 |
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