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Titlebook: Introduction to Bayesian Tracking and Particle Filters; Lawrence D. Stone,Roy L. Streit,Stephen L. Anderso Book 2023 The Editor(s) (if app

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發(fā)表于 2025-3-21 18:18:23 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書(shū)目名稱Introduction to Bayesian Tracking and Particle Filters
編輯Lawrence D. Stone,Roy L. Streit,Stephen L. Anderso
視頻videohttp://file.papertrans.cn/474/473462/473462.mp4
概述Provides a quick and insightful introduction to Bayesian Particle Filtering.Requires only basic knowledge of probability and statistics.Illustrates and motivates cardinal concepts with practical examp
叢書(shū)名稱Studies in Big Data
圖書(shū)封面Titlebook: Introduction to Bayesian Tracking and Particle Filters;  Lawrence D. Stone,Roy L. Streit,Stephen L. Anderso Book 2023 The Editor(s) (if app
描述.This book provides a quick but insightful introduction to Bayesian tracking and particle filtering for a person who has some background in probability and statistics and wishes to learn the basics of single-target tracking. It also introduces the reader to multiple target tracking by presenting useful approximate methods that are easy to implement compared to full-blown multiple target trackers..The book presents the basic concepts of Bayesian inference and demonstrates the power of the Bayesian method through numerous applications of particle filters to tracking and smoothing problems. It emphasizes target motion models that incorporate knowledge about the target’s behavior in a natural fashion rather than assumptions made for mathematical convenience..The background provided by this book allows a person to quickly become a productive member of a project team using Bayesian filtering and to develop new methods and techniques for problems the team may face..
出版日期Book 2023
關(guān)鍵詞Particle Filters; Bayesian; Tracking; Smoothing; Kalman Filter
版次1
doihttps://doi.org/10.1007/978-3-031-32242-6
isbn_softcover978-3-031-32244-0
isbn_ebook978-3-031-32242-6Series ISSN 2197-6503 Series E-ISSN 2197-6511
issn_series 2197-6503
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
The information of publication is updating

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發(fā)表于 2025-3-21 23:09:20 | 只看該作者
Bayesian Particle Filtering,, it shows that particle filters can be applied to solve problems other than tracking. The process of computing the posterior distribution on the target’s path given the measurements received in a fixed time interval . is called fixed interval smoothing and the resulting posterior distribution is th
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發(fā)表于 2025-3-22 03:00:11 | 只看該作者
Simple Multiple Target Tracking,in multiple target tracking arise when the number of targets is uncertain and there is ambiguity in deciding which target generated which measurement or whether, in fact, the measurement was generated by a false target. This chapter presents a simplified non-linear Joint Probabilistic Data Associati
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發(fā)表于 2025-3-22 08:10:13 | 只看該作者
Intensity Filters, intensity function that specifies the expected number of targets per unit state space. When this function is integrated over a subset of the state space, one obtains the expected number of targets in that subset. In Chap. ., the goal was to estimate a Bayesian probability distribution on the multip
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Lawrence D. Stone,Roy L. Streit,Stephen L. Andersonr wesentlich st?rker ausgepr?gt ist als beim Gallium. Demgegenüber tritt der lithophile Charakter weniger in Erscheinung. Das entspricht auch beispielsweise dem metallchemischen Verhalten des Indiums; die Legierungen des Indiums zeigen weit weniger ?hnlichkeit mit denen des Gruppenhomologen Gallium
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978-3-031-32244-0The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
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發(fā)表于 2025-3-23 06:59:01 | 只看該作者
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