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

Edited by

March 2020

602 pages

ISBN 978-3-03928-302-6 (Paperback)
ISBN 978-3-03928-303-3 (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

Engineering

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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