| 書目名稱 | Kalman Filtering and Information Fusion | | 編輯 | Hongbin Ma,Liping Yan,Mengyin Fu | | 視頻video | http://file.papertrans.cn/542/541749/541749.mp4 | | 概述 | Provides a comprehensive investigation into challenging problems concerning the application of Kalman filtering.Introduces state-of-art techniques and a wealth of novel results.Can serve as either a r | | 圖書封面 |  | | 描述 | This book addresses a key technology for digital information processing: Kalman filtering, which is generally considered to be one of the greatest discoveries of the 20th century. It introduces readers to issues concerning various uncertainties in a single plant, and to corresponding solutions based on adaptive estimation. Further, it discusses in detail the issues that arise when Kalman filtering technology is applied in multi-sensor systems and/or multi-agent systems, especially when various sensors are used in systems like intelligent robots, autonomous cars, smart homes, smart buildings, etc., requiring multi-sensor information fusion techniques. Furthermore, when multiple agents (subsystems) interact with one another, it produces coupling uncertainties, a challenging issue that is addressed here with the aid of novel decentralized adaptive filtering techniques..Overall, the book’s goal is to provide readers with a comprehensive investigation into the challenging problem of making Kalman filtering work well in the presence of various uncertainties and/or for multiple sensors/components. State-of-art techniques are introduced, together with a wealth of novel findings. As such, i | | 出版日期 | Book 2020 | | 關(guān)鍵詞 | Kalman filter; information fusion; uncertainty; multi-agent systems; multi-sensor systems | | 版次 | 1 | | doi | https://doi.org/10.1007/978-981-15-0806-6 | | isbn_softcover | 978-981-15-0808-0 | | isbn_ebook | 978-981-15-0806-6 | | copyright | Science Press 2020 |
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