000923627 000__ 03354cam\a2200457Ii\4500
000923627 001__ 923627
000923627 005__ 20230306151038.0
000923627 006__ m\\\\\o\\d\\\\\\\\
000923627 007__ cr\nn\nnnunnun
000923627 008__ 191129s2020\\\\si\a\\\\o\\\\\000\0\eng\d
000923627 019__ $$a1129171666$$a1130759166$$a1132876387
000923627 020__ $$a9789811508066$$q(electronic book)
000923627 020__ $$a9811508062$$q(electronic book)
000923627 020__ $$z9789811508059
000923627 020__ $$z9811508054
000923627 0248_ $$a10.1007/978-981-15-0
000923627 035__ $$aSP(OCoLC)on1129170599
000923627 035__ $$aSP(OCoLC)1129170599$$z(OCoLC)1129171666$$z(OCoLC)1130759166$$z(OCoLC)1132876387
000923627 040__ $$aLQU$$beng$$cLQU$$dGW5XE$$dYDX$$dAU@$$dGZM$$dOCLCF
000923627 049__ $$aISEA
000923627 050_4 $$aQA702.3
000923627 08204 $$a629.8
000923627 1001_ $$aMa, Hongbin.
000923627 24510 $$aKalman filtering and information fusion /$$cHongbin Ma, Liping Yan, Yuanqing Xia, Mengyin Fu.
000923627 264_1 $$aSingapore :$$bSpringer,$$c2020.
000923627 300__ $$a1 online resource (xvii, 291 pages) :$$billustrations
000923627 336__ $$atext$$btxt$$2rdacontent
000923627 337__ $$acomputer$$bc$$2rdamedia
000923627 338__ $$aonline resource$$bcr$$2rdacarrier
000923627 5050_ $$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.
000923627 506__ $$aAccess limited to authorized users.
000923627 520__ $$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 books 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.
000923627 650_0 $$aKalman filtering.
000923627 7001_ $$aYan, Liping.
000923627 7001_ $$aXia, Yuanqing.
000923627 7001_ $$aFu, Mengyin.
000923627 77608 $$iPrint version: $$z9811508054$$z9789811508059$$w(OCoLC)1119631703
000923627 852__ $$bebk
000923627 85640 $$3SpringerLink$$uhttps://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-981-15-0806-6$$zOnline Access$$91397441.1
000923627 909CO $$ooai:library.usi.edu:923627$$pGLOBAL_SET
000923627 980__ $$aEBOOK
000923627 980__ $$aBIB
000923627 982__ $$aEbook
000923627 983__ $$aOnline
000923627 994__ $$a92$$bISE