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Particle filters for random set models [electronic resource]
- 作者： Ristic, Branko.
- 其他作者：
- 出版： New York, NY : Springer New York :Imprint: Springer
- 主題： Random sets. , Stochastic processes. , Estimation theory. , Engineering. , Signal, Image and Speech Processing. , Information and Communication, Circuits. , Probability and Statistics in Computer Science. , Artificial Intelligence (incl. Robotics) , Computational Intelligence.
- ISBN： 9781461463160 (electronic bk.) 、 9781461463153 (paper)
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FIND@SFXID:
CGU
- 資料類型: 電子書
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讀者標籤：
- 系統號： 005396478 | 機讀編目格式
館藏資訊
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.
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