Multi-Sensor Information Fusion

• Egile nagusia: Gao, Yuan (auth)
• Beste egile batzuk: Jin, Xue-Bo (auth)
• Formatua: Baliabide elektronikoa Liburu kapitulua
• Hizkuntza: ingelesa
• Argitaratua: MDPI - Multidisciplinary Digital Publishing Institute 2020
• Gaiak: History of engineering & technology; similarity measure; information filter; out-of-sequence; Hellinger distance; coefficient of determination maximization strategy; uncertainty measure; embedded systems; Internet of things (IoT); random delays; adaptive distance function; random finite set; Dempster-Shafer evidence theory (DST); safe trajectory; health reliability degree; dynamic optimization; state probability approximation; sensors bias; multi-environments; belief entropy; quaternion; closed world; Gaussian process regression; Gaussian mixture model (GMM); intelligent transport system; multirotor UAV; multi-sensor system; attitude; time-domain data fusion; precision landing; Industry 4.0; magnetic angular rate and gravity (MARG) sensor; uncertainty; unscented information filter; data classification; high-definition map; global information; inconsistent data; extended belief entropy; sensor system; Steffensen's iterative method; SLAM; the Range-Range-Range frame; evidential reasoning; belief functions; powered two wheels (PTW); electronic nose; particle swarm optimization; grey group decision-making; user experience platform; complex surface measurement; DoS attack; extended Kalman filter; ICP; Gaussian density peak clustering; artificial marker; random parameter matrices; optimal estimate; local structure descriptor; object classification; domain adaption; networked systems; expectation maximization (EM) algorithm; attitude estimation; Gaussian process model; least-squares smoothing; target positioning; RFS; spectral clustering; maintenance decision; multi-target tracking; GMPHD; time-distributed ConvLSTM model; non-rigid feature matching; unknown inputs; cardiac PET; subspace alignment; gradient domain; multi-sensor measurement; data fusion; Bar-Shalom Campo; Kalman filter; signal feature extraction methods; sensor data fusion algorithm; distributed architecture; predictive modeling techniques; Gaussian mixture model; self-reporting; deep learning; mutual support degree; security zones; sensor array; soft sensor; aircraft pilot; projection; vehicle-to-everything; distributed intelligence system; square-root cubature Kalman filter; information fusion; evidence combination; LiDAR; feature representations; multi-sensor information fusion; linear constraints; galvanic skin response; decision-level sensor fusion; most suitable parameter form; Pignistic vector angle; SINS/DVL integrated navigation; fault diagnosis; facial expression; yaw estimation; dual gating; multi-sensor data fusion; multisensor system; A* search algorithm; data fusion architectures; drift compensation; augmented state Kalman filtering (ASKF); manifold; nested iterative method; data preprocessing; interference suppression; conflicting evidence; sonar network; Gaussian process; health management decision; state estimation; eye-tracking; high-dimensional fusion data (HFD); MEMS accelerometer and gyroscope; multitarget tracking; gaussian mixture probability hypothesis density; integer programming; image registration; Dempster-Shafer evidence theory; linear regression; data association; nonlinear system; covariance matrix; multi-source data fusion; fuzzy neural network; least-squares filtering; fire source localization; network flow theory; weight maps; camera; plane matching; calibration; unmanned aerial vehicle; fixed-point filter; workload; intelligent and connected vehicles; mimicry security switch strategy; alumina concentration; the Range-Point-Range frame; spatiotemporal feature learning; distributed fusion; user experience evaluation; image fusion; vehicular localization; sensor fusion; vibration; parameter learning; weighted fusion estimation; data registration; pose estimation; surface quality control; trajectory reconstruction; land vehicle; square root; Deng entropy; multi-focus; EEG; low-cost sensors; sensor fusing; sensor data fusion; packet dropouts; estimation; industrial cyber-physical system (ICPS); multi-sensor time series; multi-sensor network; Human Activity Recognition (HAR); transfer; multisensor data fusion; convergence condition; interaction tracker; acoustic emission; Covariance Projection method; mix-method approach; orthogonal redundant inertial measurement units; sematic segmentation; Surface measurement; conflict measurement; user experience measurement; observable degree analysis; open world; novel belief entropy; cutting forces; machine health monitoring; Bayesian reasoning method; orientation; surface modelling; hybrid adaptive filtering; supervoxel; RTS smoother; fast guided filter.; multi-sensor joint calibration; principal component analysis
• Sarrera elektronikoa: DOAB: download the publication; DOAB: description of the publication

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...