LEADER 04103oam 2200577I 450 001 9910149365403321 005 20240501153417.0 010 $a1-315-35175-7 010 $a1-5231-0831-2 010 $a1-315-36879-X 010 $a1-4987-4580-6 024 7 $a10.1201/9781315368795 035 $a(CKB)3710000000933768 035 $a(MiAaPQ)EBC4732243 035 $a(OCoLC)966385778 035 $a(BIP)72251033 035 $a(BIP)54932734 035 $a(EXLCZ)993710000000933768 100 $a20180331h20172017 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $2rdacontent 182 $2rdamedia 183 $2rdacarrier 200 00$aMultisensor attitude estimation $efundamental concepts and applications /$fedited by Hassen Fourati, University Grenoble Alpes, Grenoble, France, and Djamel Eddine Chouaib Belkhiat, Universite Ferhat Abbas-Setif 1, Setif, Algeria ; Krzysztof In 205 $a1st ed. 210 1$aBoca Raton :$cTaylor & Francis, CRC Press,$d[2017] 210 4$dİ2017 215 $a1 online resource (607 pages) $cillustrations 225 1 $aDevices, Circuits, and Systems 311 08$a1-4987-4571-7 320 $aIncludes bibliographical references at the end of each chapters and index. 327 $aSection I. Preliminaries on attitude representations and rotations -- Section II. Multisensor filtering for attitude estimation : theories and applications. 330 $aThere has been an increasing interest in multi-disciplinary research on multisensor attitude estimation technology driven by its versatility and diverse areas of application, such as sensor networks, robotics, navigation, video, biomedicine, etc. Attitude estimation consists of the determination of rigid bodies' orientation in 3D space. This research area is a multilevel, multifaceted process handling the automatic association, correlation, estimation, and combination of data and information from several sources. Data fusion for attitude estimation is motivated by several issues and problems, such as data imperfection, data multi-modality, data dimensionality, processing framework, etc. While many of these problems have been identified and heavily investigated, no single data fusion algorithm is capable of addressing all the aforementioned challenges. The variety of methods in the literature focus on a subset of these issues to solve, which would be determined based on the application in hand. Historically, the problem of attitude estimation has been introduced by Grace Wahba in 1965 within the estimate of satellite attitude and aerospace applications. This book intends to provide the reader with both a generic and comprehensive view of contemporary data fusion methodologies for attitude estimation, as well as the most recent researches and novel advances on multisensor attitude estimation task. It explores the design of algorithms and architectures, benefits, and challenging aspects, as well as a broad array of disciplines, including: navigation, robotics, biomedicine, motion analysis, etc. A number of issues that make data fusion for attitude estimation a challenging task, and which will be discussed through the different chapters of the book, are related to: 1) The nature of sensors and information sources (accelerometer, gyroscope, magnetometer, GPS, inclinometer, etc.); 2) The computational ability at the sensors; 3) The theoretical developments and convergence proofs; 4) The system architecture, computational resources, fusion level. 410 0$aDevices, circuits, and systems. 606 $aMotion detectors 606 $aMultisensor data fusion 606 $aElectronics in navigation 615 0$aMotion detectors. 615 0$aMultisensor data fusion. 615 0$aElectronics in navigation. 676 $a681/.2 702 $aFourati$b Hassen 702 $aBelkhiat$b Djamel Eddine Chouaib 702 $aIniewski$b Krzysztof 801 0$bFlBoTFG 801 1$bFlBoTFG 906 $aBOOK 912 $a9910149365403321 996 $aMultisensor attitude estimation$92119416 997 $aUNINA