| 书目名称 | Particle Filters for Random Set Models |
| 编辑 | Branko Ristic |
| 视频video | |
| 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 |
| 图书封面 |  |
| 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 |
| 关键词 | 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 |
发表于 2025-3-21 19:26:29
