Multi-Sensor Information Fusion
This book includes papers from the section "Multisensor Information Fusion", from Sensors between 2018 to 2019. It focuses on the latest research results of current multi-sensor fusion technologies and represents the latest research trends, including traditional information fusion technolo...
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Format: | Electronic Book Chapter |
Language: | English |
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MDPI - Multidisciplinary Digital Publishing Institute
2020
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Online Access: | DOAB: download the publication DOAB: description of the publication |
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001 | doab_20_500_12854_54050 | ||
005 | 20210211 | ||
003 | oapen | ||
006 | m o d | ||
007 | cr|mn|---annan | ||
008 | 20210211s2020 xx |||||o ||| 0|eng d | ||
020 | |a books978-3-03928-303-3 | ||
020 | |a 9783039283033 | ||
020 | |a 9783039283026 | ||
040 | |a oapen |c oapen | ||
024 | 7 | |a 10.3390/books978-3-03928-303-3 |c doi | |
041 | 0 | |a eng | |
042 | |a dc | ||
072 | 7 | |a TBX |2 bicssc | |
100 | 1 | |a Gao, Yuan |4 auth | |
700 | 1 | |a Jin, Xue-Bo |4 auth | |
245 | 1 | 0 | |a Multi-Sensor Information Fusion |
260 | |b MDPI - Multidisciplinary Digital Publishing Institute |c 2020 | ||
300 | |a 1 electronic resource (602 p.) | ||
336 | |a text |b txt |2 rdacontent | ||
337 | |a computer |b c |2 rdamedia | ||
338 | |a online resource |b cr |2 rdacarrier | ||
506 | 0 | |a Open Access |2 star |f Unrestricted online access | |
520 | |a This book includes papers from the section "Multisensor Information Fusion", from Sensors between 2018 to 2019. It focuses on the latest research results of current multi-sensor fusion technologies and represents the latest research trends, including traditional information fusion technologies, estimation and filtering, and the latest research, artificial intelligence involving deep learning. | ||
540 | |a Creative Commons |f https://creativecommons.org/licenses/by-nc-nd/4.0/ |2 cc |4 https://creativecommons.org/licenses/by-nc-nd/4.0/ | ||
546 | |a English | ||
650 | 7 | |a History of engineering & technology |2 bicssc | |
653 | |a similarity measure | ||
653 | |a information filter | ||
653 | |a out-of-sequence | ||
653 | |a Hellinger distance | ||
653 | |a coefficient of determination maximization strategy | ||
653 | |a uncertainty measure | ||
653 | |a embedded systems | ||
653 | |a Internet of things (IoT) | ||
653 | |a random delays | ||
653 | |a adaptive distance function | ||
653 | |a random finite set | ||
653 | |a Dempster-Shafer evidence theory (DST) | ||
653 | |a safe trajectory | ||
653 | |a health reliability degree | ||
653 | |a dynamic optimization | ||
653 | |a state probability approximation | ||
653 | |a sensors bias | ||
653 | |a multi-environments | ||
653 | |a belief entropy | ||
653 | |a quaternion | ||
653 | |a closed world | ||
653 | |a Gaussian process regression | ||
653 | |a Gaussian mixture model (GMM) | ||
653 | |a intelligent transport system | ||
653 | |a multirotor UAV | ||
653 | |a multi-sensor system | ||
653 | |a attitude | ||
653 | |a time-domain data fusion | ||
653 | |a precision landing | ||
653 | |a Industry 4.0 | ||
653 | |a magnetic angular rate and gravity (MARG) sensor | ||
653 | |a uncertainty | ||
653 | |a unscented information filter | ||
653 | |a data classification | ||
653 | |a high-definition map | ||
653 | |a global information | ||
653 | |a inconsistent data | ||
653 | |a extended belief entropy | ||
653 | |a sensor system | ||
653 | |a Steffensen's iterative method | ||
653 | |a SLAM | ||
653 | |a the Range-Range-Range frame | ||
653 | |a evidential reasoning | ||
653 | |a belief functions | ||
653 | |a powered two wheels (PTW) | ||
653 | |a electronic nose | ||
653 | |a particle swarm optimization | ||
653 | |a grey group decision-making | ||
653 | |a user experience platform | ||
653 | |a complex surface measurement | ||
653 | |a DoS attack | ||
653 | |a extended Kalman filter | ||
653 | |a ICP | ||
653 | |a Gaussian density peak clustering | ||
653 | |a artificial marker | ||
653 | |a random parameter matrices | ||
653 | |a optimal estimate | ||
653 | |a local structure descriptor | ||
653 | |a object classification | ||
653 | |a domain adaption | ||
653 | |a networked systems | ||
653 | |a expectation maximization (EM) algorithm | ||
653 | |a attitude estimation | ||
653 | |a Gaussian process model | ||
653 | |a least-squares smoothing | ||
653 | |a target positioning | ||
653 | |a RFS | ||
653 | |a spectral clustering | ||
653 | |a maintenance decision | ||
653 | |a multi-target tracking | ||
653 | |a GMPHD | ||
653 | |a time-distributed ConvLSTM model | ||
653 | |a non-rigid feature matching | ||
653 | |a unknown inputs | ||
653 | |a cardiac PET | ||
653 | |a subspace alignment | ||
653 | |a gradient domain | ||
653 | |a multi-sensor measurement | ||
653 | |a data fusion | ||
653 | |a Bar-Shalom Campo | ||
653 | |a Kalman filter | ||
653 | |a signal feature extraction methods | ||
653 | |a sensor data fusion algorithm | ||
653 | |a distributed architecture | ||
653 | |a predictive modeling techniques | ||
653 | |a Gaussian mixture model | ||
653 | |a self-reporting | ||
653 | |a deep learning | ||
653 | |a mutual support degree | ||
653 | |a security zones | ||
653 | |a sensor array | ||
653 | |a soft sensor | ||
653 | |a aircraft pilot | ||
653 | |a projection | ||
653 | |a vehicle-to-everything | ||
653 | |a distributed intelligence system | ||
653 | |a square-root cubature Kalman filter | ||
653 | |a information fusion | ||
653 | |a evidence combination | ||
653 | |a LiDAR | ||
653 | |a feature representations | ||
653 | |a multi-sensor information fusion | ||
653 | |a linear constraints | ||
653 | |a galvanic skin response | ||
653 | |a decision-level sensor fusion | ||
653 | |a most suitable parameter form | ||
653 | |a Pignistic vector angle | ||
653 | |a SINS/DVL integrated navigation | ||
653 | |a fault diagnosis | ||
653 | |a facial expression | ||
653 | |a yaw estimation | ||
653 | |a dual gating | ||
653 | |a multi-sensor data fusion | ||
653 | |a multisensor system | ||
653 | |a A* search algorithm | ||
653 | |a data fusion architectures | ||
653 | |a drift compensation | ||
653 | |a augmented state Kalman filtering (ASKF) | ||
653 | |a manifold | ||
653 | |a nested iterative method | ||
653 | |a data preprocessing | ||
653 | |a interference suppression | ||
653 | |a conflicting evidence | ||
653 | |a sonar network | ||
653 | |a Gaussian process | ||
653 | |a health management decision | ||
653 | |a state estimation | ||
653 | |a eye-tracking | ||
653 | |a high-dimensional fusion data (HFD) | ||
653 | |a MEMS accelerometer and gyroscope | ||
653 | |a multitarget tracking | ||
653 | |a gaussian mixture probability hypothesis density | ||
653 | |a integer programming | ||
653 | |a image registration | ||
653 | |a Dempster-Shafer evidence theory | ||
653 | |a linear regression | ||
653 | |a data association | ||
653 | |a nonlinear system | ||
653 | |a covariance matrix | ||
653 | |a multi-source data fusion | ||
653 | |a fuzzy neural network | ||
653 | |a least-squares filtering | ||
653 | |a fire source localization | ||
653 | |a network flow theory | ||
653 | |a weight maps | ||
653 | |a camera | ||
653 | |a plane matching | ||
653 | |a calibration | ||
653 | |a unmanned aerial vehicle | ||
653 | |a fixed-point filter | ||
653 | |a workload | ||
653 | |a intelligent and connected vehicles | ||
653 | |a mimicry security switch strategy | ||
653 | |a alumina concentration | ||
653 | |a the Range-Point-Range frame | ||
653 | |a spatiotemporal feature learning | ||
653 | |a distributed fusion | ||
653 | |a user experience evaluation | ||
653 | |a image fusion | ||
653 | |a vehicular localization | ||
653 | |a sensor fusion | ||
653 | |a vibration | ||
653 | |a parameter learning | ||
653 | |a weighted fusion estimation | ||
653 | |a data registration | ||
653 | |a pose estimation | ||
653 | |a surface quality control | ||
653 | |a trajectory reconstruction | ||
653 | |a land vehicle | ||
653 | |a square root | ||
653 | |a Deng entropy | ||
653 | |a multi-focus | ||
653 | |a EEG | ||
653 | |a low-cost sensors | ||
653 | |a sensor fusing | ||
653 | |a sensor data fusion | ||
653 | |a packet dropouts | ||
653 | |a estimation | ||
653 | |a industrial cyber-physical system (ICPS) | ||
653 | |a multi-sensor time series | ||
653 | |a multi-sensor network | ||
653 | |a Human Activity Recognition (HAR) | ||
653 | |a transfer | ||
653 | |a multisensor data fusion | ||
653 | |a convergence condition | ||
653 | |a interaction tracker | ||
653 | |a acoustic emission | ||
653 | |a Covariance Projection method | ||
653 | |a mix-method approach | ||
653 | |a orthogonal redundant inertial measurement units | ||
653 | |a sematic segmentation | ||
653 | |a Surface measurement | ||
653 | |a conflict measurement | ||
653 | |a user experience measurement | ||
653 | |a observable degree analysis | ||
653 | |a open world | ||
653 | |a novel belief entropy | ||
653 | |a cutting forces | ||
653 | |a machine health monitoring | ||
653 | |a Bayesian reasoning method | ||
653 | |a orientation | ||
653 | |a surface modelling | ||
653 | |a hybrid adaptive filtering | ||
653 | |a supervoxel | ||
653 | |a RTS smoother | ||
653 | |a Dempster-Shafer evidence theory (DST) | ||
653 | |a fast guided filter. | ||
653 | |a multi-sensor joint calibration | ||
653 | |a principal component analysis | ||
856 | 4 | 0 | |a www.oapen.org |u https://mdpi.com/books/pdfview/book/2121 |7 0 |z DOAB: download the publication |
856 | 4 | 0 | |a www.oapen.org |u https://directory.doabooks.org/handle/20.500.12854/54050 |7 0 |z DOAB: description of the publication |