000			03726nam a22004695i 4500
710	2		_aSpringerLink (Online service) _9106996
999			_c118576 _d118576
001			118576
003			ES-MaUEC
005			20230102113901.0
006			a||||fo|||| 00| 0
007			cr nn nnnaamaa
008			191127s2020 si a o |||| 0|eng d
020			_a9789811508066
024	7		_a10.1007/978-981-15-0806-6 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQA76 _b2020 EB
100	1		_aMa, Hongbin. _eautor _9100871
245	1	0	_aKalman Filtering and Information Fusion _cby Hongbin Ma, Liping Yan, Yuanqing Xia, Mengyin Fu.
250			_a1st ed. 2020.
264		1	_aSingapore _bSpringer Singapore : _bImprint: Springer _c2020.
300			_a1 recurso en línea (XVII, 291 páginas) _b101 ilustraciones, 38 ilustraciones a color.
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_atext file _bPDF
490	0		_aIntelligent Technologies and Robotics (Springer-42732)
505	0		_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.
520	3		_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.
988			_aPrimersemestre_2020_Robotics
650		7	_2embne _aProceso de datos _9141180
650		7	_2embne _9670999 _aKalman, Filtro de
700	1		_aYan, Liping _eautor. _4aut _4http://id.loc.gov/vocabulary/relators/aut
700	1		_aXia, Yuanqing _eautor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _9671209
700	1		_aFu, Mengyin _eautor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _9100872
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9789811508059
776	0	8	_iPrinted edition: _z9789811508073
776	0	8	_iPrinted edition: _z9789811508080
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-981-15-0806-6 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE _n0
998			_b03/2020 _dz _ek _zSI