LEADER 04457nam 22007095 450 001 9910484519603321 005 20200702005810.0 010 $a981-15-0806-2 024 7 $a10.1007/978-981-15-0806-6 035 $a(CKB)4100000009940020 035 $a(MiAaPQ)EBC5986811 035 $a(DE-He213)978-981-15-0806-6 035 $a(PPN)243768400 035 $a(EXLCZ)994100000009940020 100 $a20191127d2020 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aKalman Filtering and Information Fusion /$fby Hongbin Ma, Liping Yan, Yuanqing Xia, Mengyin Fu 205 $a1st ed. 2020. 210 1$aSingapore :$cSpringer Singapore :$cImprint: Springer,$d2020. 215 $a1 online resource (xvii, 291 pages) $cillustrations 311 $a981-15-0805-4 320 $aIncludes bibliographical references. 327 $aPreface -- 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. 330 $aThis 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. 606 $aControl engineering 606 $aRobotics 606 $aMechatronics 606 $aApplied mathematics 606 $aEngineering mathematics 606 $aSystem theory 606 $aElectrical engineering 606 $aControl, Robotics, Mechatronics$3https://scigraph.springernature.com/ontologies/product-market-codes/T19000 606 $aMathematical and Computational Engineering$3https://scigraph.springernature.com/ontologies/product-market-codes/T11006 606 $aSystems Theory, Control$3https://scigraph.springernature.com/ontologies/product-market-codes/M13070 606 $aElectrical Engineering$3https://scigraph.springernature.com/ontologies/product-market-codes/T24000 615 0$aControl engineering. 615 0$aRobotics. 615 0$aMechatronics. 615 0$aApplied mathematics. 615 0$aEngineering mathematics. 615 0$aSystem theory. 615 0$aElectrical engineering. 615 14$aControl, Robotics, Mechatronics. 615 24$aMathematical and Computational Engineering. 615 24$aSystems Theory, Control. 615 24$aElectrical Engineering. 676 $a629.8312 700 $aMa$b Hongbin$4aut$4http://id.loc.gov/vocabulary/relators/aut$0720640 702 $aYan$b Liping$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aXia$b Yuanqing$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aFu$b Mengyin$4aut$4http://id.loc.gov/vocabulary/relators/aut 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910484519603321 996 $aKalman Filtering and Information Fusion$92855130 997 $aUNINA