Multisensor Fusion and Integration in the Wake of Big Data, Deep Learning and Cyber Physical System

Name: Multisensor Fusion and Integration in the Wake of Big Data, Deep Learning and Cyber Physical System | An Edition of the Selected Papers from the 2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2017)
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An Edition of the Selected Papers from the 2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2017)

Sukhan Lee Hanseok Ko Songhwai Oh(Editor)

Springer (Publisher)

1st Edition

Published on 4. July 2018

VIII, 300 pages

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978-3-319-90509-9 (ISBN)

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Content

Intro
Preface
Contents
Multi-sensor Fusion: Theory and Practice
Covariance Projection as a General Framework of Data Fusion and Outlier Removal
Abstract
1 Introduction
1.1 Problem Statement
2 Proposed Approach
3 Confidence Measure of Data Sources
3.1 Inconsistency Detection and Exclusion
3.2 Effect of Correlation on d Distance
4 Simulation Results
5 Conclusion
Acknowledgments
Appendix 1
Appendix 2
References
State Estimation in Networked Control Systems with Delayed and Lossy Acknowledgments
1 Introduction
2 Problem Formulation
3 Derivation of the Proposed Estimator
3.1 Modeling the NCS as a Markov Jump Linear System
3.2 Estimator Design
4 Evaluation
5 Conclusions
References
Performance of State Estimation and Fusion with Elliptical Motion Constraints
1 Introduction
2 System Model
2.1 Coordinated Turn (CT) Model
2.2 Elliptical Constraint
2.3 Generating Constrained States
3 Projection-Based Constrained Estimation
3.1 Direct Connection to Ellipse Center
3.2 Shortest Distance to Unconstrained Estimate
4 Fusion of Constrained Estimates
4.1 Fusion Rules
4.2 Fusion Rules with Constrained Estimates
5 Constrained Fusion with Information Loss
5.1 Simulation Setup
5.2 Performance
6 Conclusions
References
Relevance and Redundancy as Selection Techniques for Human-Autonomy Sensor Fusion
1 Introduction
2 Related Work
3 Theory and Background
3.1 Preliminaries
3.2 Relevance
3.3 Redundancy
3.4 Relevance and Redundancy with Specific Fusion Algorithms
4 Empirical Tests
4.1 Redundancy
4.2 Relevance
4.3 Redundancy vs. Relevance
5 Conclusions and Future Work
References
Classification of Reactor Facility Operational State Using SPRT Methods with Radiation Sensor Networks
1 Introduction
2 Detection Problem
2.1 SPRT Detection
2.2 Stack Intensity Estimation
3 IRSS Experimental Results
3.1 IRSS Datasets
3.2 Experimental SPRTs
3.3 Performance Comparison
4 HFIR Experimental Results
4.1 HFIR Datasets and Experimental SPRTs
4.2 Performance Comparison
5 Performance of IE SPRT Detection Method
5.1 Single Location Measurements
5.2 Network Measurements
6 Conclusion
References
Improving Ego-Lane Detection by Incorporating Source Reliability
1 Introduction
2 Related Work
2.1 Multi-source Fusion for Ego-Lane Detection
2.2 Reliability in Fusion
3 Concept of Reliability-Aware Ego-Lane Detection
4 Reliability for Ego-Lane Detection
4.1 Requirements
4.2 Sensor-Independent Performance Measure
5 Learning Reliabilities of Ego-Lane Estimations
5.1 Learning Reliability Using Classifiers
5.2 Training Data for the Classifiers
5.3 Feature Selection
5.4 Applying Classifiers Towards Learning Reliability
6 Reliability-Aware Ego-Lane Fusion
6.1 Dempster-Shafer Theory (DST):
6.2 Other Fusion Approaches
7 Experimental Evaluation
7.1 Assessment of Reliability Estimation
7.2 Assessment Information Fusion
7.3 Exemplary Results
8 Conclusion
References
Applying Knowledge-Based Reasoning for Information Fusion in Intelligence, Surveillance, and Reconnaissance
1 Introduction
2 Approach
2.1 System Overview
2.2 Information Fusion
3 Knowledge Modeling
3.1 Types of Knowledge
3.2 Building the Knowledge Model
4 Architecture of the Information Fusion Component
5 Information Extraction
5.1 Current Information and Background Information
5.2 Sensor Data
6 Information Integration and Management
6.1 The Object-Oriented World Model
6.2 Probabilistic Information Processing in the OOWM
6.3 External Interfaces for the OOWM
7 Logical Reasoning
7.1 Logical Inference
7.2 Choice of Logic
7.3 Tool Support
7.4 Model Transformation
7.5 Further Considerations
8 Probabilistic Reasoning
8.1 Probabilistic Models
8.2 Probabilistic Relational Models
8.3 Model Extensions
8.4 Transformation Toolchain
8.5 Exemplary Qualitative Results
9 Agent-Based Architecture Realization
9.1 Suitability of Software Agents for Information Fusion
9.2 Agent-Based Information Extraction
9.3 Further Extensions
10 Conclusion and Future Work
References
Multiple Classifier Fusion Based on Testing Sample Pairs
Abstract
1 Introduction
2 Overview of Multiple Classifier Systems
2.1 Multiple Classifier Systems
2.2 The Methods of Generating Member Classifier
3 Design of Multiple Neighborhood-Based Classifier Systems Based on Testing Sample Pairs
3.1 Neighborhood-Based Classifier Based on Testing Sample Pairs
3.2 Multiple Classifier System Based on Evidential Reasoning Based OWA
4 Experiments
4.1 Classification Experiments Based on Artificial Dataset
4.2 Classification Experiments Based on UCI Datasets
5 Conclusion
References
Multi-sensor Fusion Applications in Robotics
Multi-sensor Fusion Applications in Robotics
Bayesian Estimator Based Target Localization in Ship Monitoring System Using Multiple Compact High ...
Abstract
1 Introduction
2 Bayesian Estimator
3 Proposed Method
3.1 Ship Detection
3.2 Target Region Modelling
3.3 Model Association
3.4 Final Location Estimation
4 Experiments
4.1 Simulation Test
4.2 Real Data Test
5 Conclusions
References
SLAM-Based Return to Take-Off Point for UAS
1 Introduction
2 Related Work
3 Problem Description
4 Path Planning Using Shortcuts
4.1 Problem Definition
4.2 Algorithm Description
4.3 Algorithm Convergence
5 Evaluation
5.1 Numerical Studies
5.2 Simulations
5.3 Real-World Experiments
6 Summary
References
Underwater Terrain Navigation During Realistic Scenarios
1 Introduction
2 Related Work
2.1 Depth Data in the Particle Filter
2.2 Magnetic Data in the Particle Filter
2.3 Using Other Data in the Particle Filter
3 Limitations with Current Research
4 Combining Depth and Magnetic Data
4.1 Kalman Filter for Enhancing the Performance
4.2 High Level Algorithm
5 Evaluation and Tuning of the Algorithm
5.1 Test Setup
5.2 Example Images from Running the Program
5.3 Test 1 - Comparing Subset Methods
5.4 Test 2 - Comparing Various Mixes of Subset Methods
5.5 Test 3 - Comparing Various Mixes of Subset Methods for a High-Accuracy INS
5.6 Test 4 - Investigating the Performance When a Portion of Particles are Dead Reckoned
5.7 Test 5 - Investigating the Performance When a Portion of Particles are Dead Reckoned
5.8 Test 6 - Comparing Performance When Not Using the Bottom Depth Lines
5.9 Further Development and Testing of the Algorithm
6 Conclusion
References
Supervised Calibration Method for Improving Contrast and Intensity of LIDAR Laser Beams
Abstract
1 Introduction
2 Mapping System
3 Localization System
4 Calibration System
4.1 Problem Statement
4.2 Proposed Method
5 Experimental Results and Discussion
6 Conclusion
References
Multi-object Tracking Based on a Multi-layer Particle Filter for Unclustered Spatially Extended Measurements
1 Introduction
2 Related Work
3 Multi-layer Particle Filter for Tracking Applications
3.1 Fundamentals of SIR Particle Filter
3.2 Tracking with Multi-layer Particle Filter
4 Particle Clustering
4.1 Expectation Maximization Clustering
4.2 Estimation of the Current Number of Clusters
4.3 Cluster Prediction
5 Experimental Studies
6 Conclusion and Future Work
6.1 Conclusion
6.2 Future Work
References
Ensemble Kalman Filter Variants for Multi-Object Tracking with False and Missing Measurements
1 Introduction
2 Ensemble Kalman Filter
3 EnKF for Multi-Object Tracking
3.1 Problem Formulation
3.2 OSPA-EnKF for MOT
3.3 JPDA-EnKF for MOT
4 Evaluation
5 Conclusion and Outlook
References
Fall Detection with Unobtrusive Infrared Array Sensors
1 Introduction
2 Related Work
3 Fall Detection Classifiers
4 Performance Evaluation
5 Conclusion
References
Subtle Hand Action Recognition in Factory Based on Inertial Sensors
Abstract
1 Introduction
2 Related Work
3 Architecture and Experiment Setup
3.1 Hardware Description
3.2 Software Architecture
4 Data Segmentation
4.1 Data Acquisition
4.2 Hand Action Segmentation
5 Extract Feature and Description of Classifier
6 Experiment
7 Conclusion
Acknowledgment
References
Kinematics, Dynamics and Control of an Upper Limb Rehabilitation Exoskeleton
Abstract
1 Introduction
2 Hardware of Exoskeleton
3 Kinematics of Exoskeleton
4 Dynamics of Exoskeleton
5 Admittance Control Strategy
6 Conclusion
Acknowledgement
References
Author Index

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Multisensor Fusion and Integration in the Wake of Big Data, Deep Learning and Cyber Physical System

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