Advances in Multi-Sensor Information Fusion: Theory and Applications 2017

Edited by
May 2018
568 pages
  • ISBN978-3-03842-933-3 (Paperback)
  • ISBN978-3-03842-934-0 (PDF)

This book is a reprint of the Special Issue Advances in Multi-Sensor Information Fusion: Theory and Applications 2017 that was published in

Chemistry & Materials Science
Environmental & Earth Sciences
  • Paperback
© 2019 by the authors; CC BY license
facial expression recognition; fusion features; salient facial areas; hand-crafted features; feature correction; object tracking; multiple features; posterior probability measure; centroid iteration; maneuvering target tracking; spherical simplex-radial rule; cubature Kalman filter; fading factor; strong tracking filter; random finite set Bayesian filtering; OpenCL; real-time execution; DS evidence theory; state estimation; liquid level measurement; alarm monitoring; bearings-only target tracking; statistical linear regression; auxiliary truncated unscented Kalman filtering; flexible fusion structure; mixed fusion method; combinatorial optimization; sensor subsets selection; tracking accuracy; system survivability; data fusion; data registration; intrinsic surface features; ultra-precision freeform surfaces; precision metrology; recursive fusion estimation; sensor networks; random parameter matrices; random delays; packet dropouts; largest ellipsoid; distributed data fusion; parallel structure; unknown cross-covariances; multisensor; sensor data fusion; Dempster–Shafer evidence theory; Gaussian distribution; reliability-based BBA; online denoising; the second-order adaptive statistics model; Kalman filter; Yule–Walker algorithm; real-time data processing; Dempster–Shafer evidence theory; specific emitter identification; time–space domain information fusion; quantum mechanical approach; correlation coefficient; recursive centralized model; fuzzy risk evaluation; failure mode and effects analysis; multi-sensor information fusion; D numbers; dempster-shafer evidence theory; fuzzy uncertainty; multi-level system; lifetime prediction; Bayesian Networks; multi-sensor information integration; complex logical correlation; sensor fusion; Unscented Kalman Filter (UKF); vehicle localization; Dempster–Shafer evidence theory; belief entropy; distance of evidence; IOWA operator; fault diagnosis; sensor data fusion; bias estimation; radar modeling; measurement error; air traffic control; sensor fusion; maneuvering complex extended object; coupled motion kinematics and extension dynamics; Minkowski sum; range extent measurements; clustering; clustering ensemble; closeness measure; geometric distance; neighborhood chain; nonlinear system; weighted measurement fusion; Gauss–Hermite approximation; particle filter; cooperative localization (CL); multiple autonomous underwater vehicles (multi-AUVs); information entropy; probability hypothesis density (PHD) filter; D–S evidence theory; dependent evidence; rank correlation coefficient; sensor data fusion; evidential conflict; evidence distance; belief entropy; variance of entropy; fuzzy preference relations; Dempster–Shafer evidence theory; fault diagnosis; multi-sensor tracking; data association; cubature Kalman filter; state estimation; centralized filtering; ADS-B; TCAS; integrated display; data fusion; airspace surveillance; force estimation; heterogeneous sensor network; Kalman filtering; multi-metric measurements; structural dynamics; gait identification; inertial motion analysis; spectro-temporal representation; deep convolutional neural network; multi-sensor fusion; error minimization; travel time distribution; data fusion; evidence theory; spatial correlation; uncertainty; multi-sensor fusion; non-linear filtering; GNSS-based navigation; land-vehicle localization; digital road maps; digital elevation models; n/a