04457nam 22007095 450 991048451960332120200702005810.0981-15-0806-210.1007/978-981-15-0806-6(CKB)4100000009940020(MiAaPQ)EBC5986811(DE-He213)978-981-15-0806-6(PPN)243768400(EXLCZ)99410000000994002020191127d2020 u| 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierKalman Filtering and Information Fusion /by Hongbin Ma, Liping Yan, Yuanqing Xia, Mengyin Fu1st ed. 2020.Singapore :Springer Singapore :Imprint: Springer,2020.1 online resource (xvii, 291 pages) illustrations981-15-0805-4 Includes bibliographical references.Preface -- Part I Kalman Filtering: Preliminaries -- Part II Kalman Filtering for Uncertain Systems -- Part III Kalman Filtering for Multi-Sensor Systems -- Part IV Kalman Filtering for Multi-Agent Systems.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, it can be a good reference book for researchers whose work involves filtering and applications; yet it can also serve as a postgraduate textbook for students in mathematics, engineering, automation, and related fields. To read this book, only a basic grasp of linear algebra and probability theory is needed, though experience with least squares, navigation, robotics, etc. would definitely be a plus.Control engineeringRoboticsMechatronicsApplied mathematicsEngineering mathematicsSystem theoryElectrical engineeringControl, Robotics, Mechatronicshttps://scigraph.springernature.com/ontologies/product-market-codes/T19000Mathematical and Computational Engineeringhttps://scigraph.springernature.com/ontologies/product-market-codes/T11006Systems Theory, Controlhttps://scigraph.springernature.com/ontologies/product-market-codes/M13070Electrical Engineeringhttps://scigraph.springernature.com/ontologies/product-market-codes/T24000Control engineering.Robotics.Mechatronics.Applied mathematics.Engineering mathematics.System theory.Electrical engineering.Control, Robotics, Mechatronics.Mathematical and Computational Engineering.Systems Theory, Control.Electrical Engineering.629.8312Ma Hongbinauthttp://id.loc.gov/vocabulary/relators/aut720640Yan Lipingauthttp://id.loc.gov/vocabulary/relators/autXia Yuanqingauthttp://id.loc.gov/vocabulary/relators/autFu Mengyinauthttp://id.loc.gov/vocabulary/relators/autMiAaPQMiAaPQMiAaPQBOOK9910484519603321Kalman Filtering and Information Fusion2855130UNINA