Advances in Multi-Sensor Information Fusion : : Theory and Applications 2017 / / edited by Xue-Bo Jin [and three others].

• TeilnehmendeR: Jin, Xue-Bo,
• Place / Publishing House: Basel, Switzerland : : MDPI - Multidisciplinary Digital Publishing Institute,, [2018]; ©2018
• Year of Publication: 2018
• Language: English
• Physical Description: 1 online resource (620 pages) :; illustrations

The information fusion technique can integrate a large amount of data and knowledge representing the same real-world object and obtain a consistent, accurate and useful representation of that object. The data may be independent or redundant, and can be obtained by different sensors at the same time or at different times. A suitable combination of investigative methods can substantially increase the profit of information in comparison with that from a single sensor. Multi-sensor information fusion has been a key issue in sensor research since the 1970s and it has been applied in many fields, such as geospatial information systems, business intelligence, oceanography, discovery science, intelligent transport systems, wireless sensor networks, etc. Recently, thanks to the vast development in sensor and computer memory technologies, more and more sensors are being used in practical systems and a large amount of measurement data are recorded and restored, which may actually be the "time series big data". For example, sensors in machines and process control industries can generate a lot of data, which have real, actionable business value. The fusion of these data can greatly improve productivity through digitization. The goal of this Special Issue is to report on innovative ideas and solutions for the methods of multi-sensor information fusion in the emerging applications era, focusing on development, adoption and applications.

Table of Contents

About the Special Issue Editors -- Advances in Multi-Sensor Information Fusion: Theory and Applications 2017 -- Facial Expression Recognition with Fusion Features Extracted from Salient Facial Areas -- Object Tracking Using Local Multiple Features and a PosteriorProbability Measure -- Strong Tracking Spherical Simplex-Radial Cubature Kalman Filter for Maneuvering Target Tracking -- Random Finite Set Based Bayesian Filtering with OpenCL in a Heterogeneous Platform -- State Estimation Using Dependent Evidence Fusion: Application to Acoustic ResonanceBased Liquid Level Measurement -- Auxiliary Truncated Unscented Kalman Filtering for Bearings-Only Maneuvering Target Tracking -- Flexible Fusion Structure-Based Performance OptimizationLearning for Multisensor Target Tracking -- Development of Data Registration and Fusion Methods for Measurement of Ultra-Precision Freeform Surfaces -- Optimal Fusion Estimation with Multi-Step Random Delays and Losses in Transmission -- Multisensor Parallel Largest Ellipsoid Distributed Data Fusion with Unknown Cross-Covariance -- A Reliability-Based Method to Sensor Data Fusion -- Online Denoising Based on the Second-Order Adaptive Statistics Model -- A Time-Space Domain Information Fusion Method for Specific Emitter Identification Based on Dempster-Shafer Evidence Theory -- Fuzzy Risk Evaluation in Failure Mode and Effects Analysis Using a D Numbers Based Multi-Sensor Information Fusion Method -- Modeling of BN Lifetime Prediction of a System Based on Integrated Multi-Level Information -- A Robust Vehicle Localization Approach Based on GNSS/IMU/DMI/LiDAR Sensor Fusion for Autonomous Vehicles -- An Improved Evidential-IOWA Sensor Data Fusion Approach in Fault Diagnosis -- Complete Systematic Error Model of SSR for Sensor Registration in ATC Surveillance Networks -- Tracking of Maneuvering Complex Extended Object with Coupled Motion Kinematics and Extension Dynamics Using Range Extent Measurements -- A Clustering-Oriented Closeness Measure Based on Neighborhood Chain and Its Application in the Clustering Ensemble Framework Based on the Fusion of Different Closeness Measures -- A Weighted Measurement Fusion Particle Filter for Nonlinear Multisensory Systems Based on Gauss-Hermite Approximation -- Cooperative Localization for Multi-AUVs Based on GM-PHD Filters and Information Entropy Theory -- Research on the Fusion of Dependent Evidence Based on Rank Correlation Coefficient -- A Novel Evidence Theory and Fuzzy Preference Approach-Based Multi-Sensor Data Fusion Technique for Fault Diagnosis -- Centralized Multi-Sensor Square Root Cubature Joint Probabilistic Data Association -- Integrated Display and Simulation for Automatic Dependent Surveillance-Broadcast and Traffic Collision Avoidance System Data Fusion -- Model-Based Heterogeneous Data Fusion for Reliable Force Estimation in Dynamic Structures under Uncertaintie -- IMU-Based Gait Recognition Using Convolutional Neural Networks and Multi-Sensor Fusion -- Heterogeneous Data Fusion Method to Estimate Travel Time Distributions in Congested Road Network -- A General Framework for 3-D Parameters Estimation of Roads Using GPS, OSM and DEM Data.