Multi-Sensor Information Fusion

doi:10.3390/books978-3-03928-303-3

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Keywords

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

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Multi-Sensor Information Fusion

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March 2020

602 pages

ISBN978-3-03928-302-6 (Paperback)
ISBN978-3-03928-303-3 (PDF)

https://doi.org/10.3390/books978-3-03928-303-3

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This is a Reprint of the Special Issue Multi-Sensor Information Fusion that was published in

Chemistry & Materials Science

Engineering

Environmental & Earth Sciences

Summary

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.

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