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210512 ||| eng |
| 020 |
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|a 9783039283026
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| 020 |
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|a 9783039283033
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| 020 |
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|a books978-3-03928-303-3
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| 100 |
1 |
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|a Gao, Yuan
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| 245 |
0 |
0 |
|a Multi-Sensor Information Fusion
|h Elektronische Ressource
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| 260 |
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|b MDPI - Multidisciplinary Digital Publishing Institute
|c 2020
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| 300 |
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|a 1 electronic resource (602 p.)
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| 653 |
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|a data fusion architectures
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|a multi-sensor information fusion
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| 653 |
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|a domain adaption
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| 653 |
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|a user experience evaluation
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|a augmented state Kalman filtering (ASKF)
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|a sonar network
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|a Bar-Shalom Campo
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|a network flow theory
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| 653 |
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|a fuzzy neural network
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| 653 |
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|a Industry 4.0
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|a workload
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| 653 |
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|a quaternion
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|a belief functions
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| 653 |
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|a belief entropy
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| 653 |
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|a Dempster-Shafer evidence theory (DST)
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| 653 |
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|a unmanned aerial vehicle
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|a optimal estimate
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| 653 |
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|a high-dimensional fusion data (HFD)
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| 653 |
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|a manifold
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|a dual gating
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|a galvanic skin response
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| 653 |
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|a data fusion
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|a mix-method approach
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|a deep learning
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| 653 |
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|a adaptive distance function
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| 653 |
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|a user experience measurement
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| 653 |
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|a vehicular localization
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| 653 |
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|a security zones
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| 653 |
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|a acoustic emission
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| 653 |
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|a multirotor UAV
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| 653 |
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|a uncertainty
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| 653 |
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|a facial expression
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| 653 |
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|a artificial marker
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| 653 |
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|a open world
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| 653 |
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|a Hellinger distance
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| 653 |
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|a plane matching
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| 653 |
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|a image registration
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| 653 |
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|a multi-sensor system
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|a sensors bias
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|a orthogonal redundant inertial measurement units
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| 653 |
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|a attitude
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|a multisensor system
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| 653 |
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|a RTS smoother
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| 653 |
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|a interference suppression
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| 653 |
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|a multi-sensor time series
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| 653 |
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|a square root
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| 653 |
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|a intelligent and connected vehicles
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| 653 |
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|a state estimation
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| 653 |
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|a convergence condition
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| 653 |
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|a networked systems
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|a drift compensation
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| 653 |
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|a Pignistic vector angle
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|a sensor array
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| 653 |
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|a packet dropouts
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|a dynamic optimization
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| 653 |
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|a image fusion
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| 653 |
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|a powered two wheels (PTW)
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| 653 |
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|a health reliability degree
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| 653 |
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|a land vehicle
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| 653 |
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|a sensor fusion
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| 653 |
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|a SLAM
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| 653 |
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|a multi-sensor joint calibration
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| 653 |
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|a vibration
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| 653 |
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|a state probability approximation
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| 653 |
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|a random finite set
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| 653 |
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|a pose estimation
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| 653 |
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|a observable degree analysis
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| 653 |
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|a eye-tracking
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| 653 |
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|a decision-level sensor fusion
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| 653 |
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|a Gaussian density peak clustering
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| 653 |
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|a inconsistent data
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| 653 |
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|a Kalman filter
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| 653 |
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|a electronic nose
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| 653 |
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|a aircraft pilot
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| 653 |
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|a fault diagnosis
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| 653 |
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|a gradient domain
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| 653 |
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|a low-cost sensors
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| 653 |
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|a soft sensor
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| 653 |
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|a random parameter matrices
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| 653 |
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|a evidential reasoning
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| 653 |
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|a spatiotemporal feature learning
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| 653 |
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|a yaw estimation
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| 653 |
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|a health management decision
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| 653 |
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|a grey group decision-making
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| 653 |
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|a global information
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| 653 |
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|a conflict measurement
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| 653 |
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|a gaussian mixture probability hypothesis density
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| 653 |
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|a signal feature extraction methods
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| 653 |
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|a nonlinear system
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| 653 |
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|a projection
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| 653 |
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|a cutting forces
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| 653 |
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|a predictive modeling techniques
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| 653 |
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|a spectral clustering
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| 653 |
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|a DoS attack
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| 653 |
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|a machine health monitoring
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| 653 |
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|a Gaussian mixture model (GMM)
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| 653 |
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|a sensor fusing
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| 653 |
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|a surface modelling
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| 653 |
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|a camera
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| 653 |
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|a conflicting evidence
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| 653 |
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|a coefficient of determination maximization strategy
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| 653 |
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|a weighted fusion estimation
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| 653 |
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|a unscented information filter
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| 653 |
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|a the Range-Point-Range frame
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| 653 |
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|a data association
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| 653 |
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|a particle swarm optimization
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| 653 |
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|a vehicle-to-everything
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| 653 |
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|a industrial cyber-physical system (ICPS)
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| 653 |
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|a A* search algorithm
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| 653 |
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|a Internet of things (IoT)
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| 653 |
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|a feature representations
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| 653 |
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|a unknown inputs
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| 653 |
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|a fire source localization
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| 653 |
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|a multi-source data fusion
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| 653 |
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|a Dempster–Shafer evidence theory
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| 653 |
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|a multi-target tracking
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| 653 |
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|a square-root cubature Kalman filter
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| 653 |
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|a sematic segmentation
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| 653 |
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|a sensor data fusion algorithm
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| 653 |
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|a distributed intelligence system
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| 653 |
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|a Covariance Projection method
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| 653 |
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|a Deng entropy
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| 653 |
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|a calibration
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| 653 |
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|a transfer
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| 653 |
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|a distributed fusion
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| 653 |
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|a object classification
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| 653 |
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|a covariance matrix
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| 653 |
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|a linear regression
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| 653 |
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|a orientation
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| 653 |
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|a multi-sensor data fusion
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| 653 |
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|a surface quality control
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| 653 |
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|a weight maps
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| 653 |
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|a non-rigid feature matching
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| 653 |
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|a precision landing
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| 653 |
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|a mutual support degree
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| 653 |
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|a intelligent transport system
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| 653 |
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|a embedded systems
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| 653 |
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|a complex surface measurement
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| 653 |
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|a ICP
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| 653 |
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|a Gaussian process
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| 653 |
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|a similarity measure
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| 653 |
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|a Gaussian process model
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| 653 |
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|a self-reporting
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| 653 |
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|a data registration
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| 653 |
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|a closed world
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| 653 |
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|a maintenance decision
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| 653 |
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|a nested iterative method
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| 653 |
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|a out-of-sequence
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| 653 |
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|a magnetic angular rate and gravity (MARG) sensor
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| 653 |
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|a Surface measurement
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| 653 |
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|a principal component analysis
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| 653 |
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|a least-squares smoothing
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| 653 |
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|a data classification
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| 653 |
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|a hybrid adaptive filtering
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| 653 |
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|a trajectory reconstruction
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| 653 |
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|a multitarget tracking
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| 653 |
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|a expectation maximization (EM) algorithm
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| 653 |
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|a MEMS accelerometer and gyroscope
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| 653 |
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|a supervoxel
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| 653 |
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|a uncertainty measure
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| 653 |
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|a fixed-point filter
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| 653 |
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|a integer programming
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| 653 |
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|a most suitable parameter form
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| 653 |
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|a SINS/DVL integrated navigation
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| 653 |
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|a subspace alignment
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| 653 |
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|a multi-sensor measurement
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| 653 |
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|a multisensor data fusion
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| 653 |
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|a EEG
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| 653 |
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|a local structure descriptor
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| 653 |
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|a multi-sensor network
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| 653 |
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|a GMPHD
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| 653 |
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|a time-distributed ConvLSTM model
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| 653 |
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|a mimicry security switch strategy
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| 653 |
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|a Gaussian process regression
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| 653 |
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|a linear constraints
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| 653 |
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|a the Range-Range-Range frame
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| 653 |
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|a high-definition map
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| 653 |
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|a extended Kalman filter
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| 653 |
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|a LiDAR
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| 653 |
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|a information filter
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| 653 |
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|a sensor system
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| 653 |
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|a Bayesian reasoning method
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| 653 |
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|a Steffensen’s iterative method
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| 653 |
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|a target positioning
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| 653 |
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|a estimation
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| 653 |
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|a user experience platform
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| 653 |
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|a multi-focus
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| 653 |
|
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|a Dempster–Shafer evidence theory (DST)
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| 653 |
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|a novel belief entropy
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| 653 |
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|a time-domain data fusion
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| 653 |
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|a safe trajectory
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| 653 |
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|a extended belief entropy
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| 653 |
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|a data preprocessing
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| 653 |
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|a random delays
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| 653 |
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|a RFS
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| 653 |
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|a Gaussian mixture model
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| 653 |
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|a multi-environments
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| 653 |
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|a distributed architecture
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| 653 |
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|a information fusion
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| 653 |
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|a alumina concentration
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| 653 |
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|a interaction tracker
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| 653 |
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|a attitude estimation
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| 653 |
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|a cardiac PET
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| 653 |
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|a Human Activity Recognition (HAR)
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| 653 |
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|a fast guided filter.
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| 653 |
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|a parameter learning
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| 653 |
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|a least-squares filtering
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| 653 |
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|a evidence combination
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| 653 |
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|a sensor data fusion
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| 700 |
1 |
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|a Jin, Xue-Bo
|
| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b DOAB
|a Directory of Open Access Books
|
| 500 |
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|a Creative Commons (cc), https://creativecommons.org/licenses/by-nc-nd/4.0/
|
| 024 |
8 |
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|a 10.3390/books978-3-03928-303-3
|
| 856 |
4 |
0 |
|u https://directory.doabooks.org/handle/20.500.12854/54050
|3 Volltext
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|a 720
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|a 363
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|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.
|