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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 System3.2 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 Setup5.2 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 Estimation3 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 Detection4.1 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
3.1 Modeling the NCS as a Markov Jump Linear System3.2 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 Setup5.2 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 Estimation3 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 Detection4.1 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