04285nam 22007335 450 991048451960332120251127082720.0981-15-0806-210.1007/978-981-15-0806-6(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 Nature 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.Automatic controlRoboticsAutomationEngineering mathematicsEngineeringData processingSystem theoryControl theoryElectrical engineeringControl, Robotics, AutomationMathematical and Computational Engineering ApplicationsSystems Theory, ControlElectrical and Electronic EngineeringAutomatic control.Robotics.Automation.Engineering mathematics.EngineeringData processing.System theory.Control theory.Electrical engineering.Control, Robotics, Automation.Mathematical and Computational Engineering Applications.Systems Theory, Control.Electrical and Electronic 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