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Uncertainty Theories and Multisensor Data Fusion

Alain Appriou (Editor)
ISBN: 978-1-84821-354-8
288 pages
June 2014, Wiley-ISTE
Uncertainty Theories and Multisensor Data Fusion (1848213549) cover image
Addressing recent challenges and developments in this growing field, Multisensor Data Fusion Uncertainty Theory first discusses basic questions such as: Why and when is multiple sensor fusion necessary? How can the available measurements be characterized in such a case? What is the purpose and the specificity of information fusion processing in multiple sensor systems? Considering the different uncertainty formalisms, a set of coherent operators corresponding to the different steps of a complete fusion process is then developed, in order to meet the requirements identified in the first part of the book.
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Introduction ix

Chapter 1 Multisensor Data Fusion 1

1.1 Issues at stake 1

1.2 Problems 4

1.3 Solutions 21

1.4 Position of multisensor data fusion 27

Chapter 2 Reference Formalisms 31

2.1 Probabilities 31

2.2 Fuzzy sets 35

2.3 Possibility theory 39

2.4 Belief functions theory 43

Chapter 3 Set Management and Information Propagation 53

3.1 Fuzzy sets: propagation of imprecision 53

3.2 Probabilities and possibilities: the same approach to uncertainty 56

3.3 Belief functions: an overarching vision in terms of propagation 57

3.4 Example of applications: updating of knowledge over time 66

Chapter 4 Managing the Reliability of Information 71

4.1 Possibilistic view 72

4.2 Discounting of belief functions 73

4.3 Integrated processing of reliability 75

4.4 Management of domains of validity of the sources 77

4.5 Application to fusion of pixels from multispectral images 82

4.6 Formulation for problems of estimation 87

Chapter 5 Combination of Sources 91

5.1 Probabilities: a turnkey solution, Bayesian inference 92

5.2 Fuzzy sets: a grasp of axiomatics 94

5.3 Possibility theory: a simple approach to the basic principles 102

5.4 Theory of belief functions: conventional approaches 106

5.5 General approach to combination: any sets and logics 113

5.6 Conflict management 118

5.7 Back to Zadeh's paradox 122

Chapter 6 Data Modeling 127

6.1 Characterization of signals 127

6.2 Probabilities: immediate taking into account 130

6.3 Belief functions: an open-ended and overarching framework 131

6.4 Possibilities: a similar approach 153

6.5 Application to a didactic example of classification 157

Chapter 7 Classification: Decision-Making and Exploitation of the Diversity of Information Sources 165

7.1 Decision-making: choice of the most likely hypothesis 166

7.2 Decision-making: determination of the most likely set of hypotheses 168

7.3 Behavior of the decision operator: some practical examples 171

7.4 Exploitation of the diversity of information sources: integration of binary comparisons 175

7.5 Exploitation of the diversity of distinct but overlapping sets 179

7.6 Exploitation of the diversity of the attributes: example of application to the fusion of airborne image data 189

Chapter 8 Spatial Dimension: Data-Association 193

8.1 Data association: a multiform problem, which is unavoidable in multisensor data fusion 194

8.2 Construction of a general method for data association 197

8.3 Simple example of the implementation of the method 203

Chapter 9 Temporal Dimension: Tracking 211

9.1 Tracking: exploitation of the benefits of multisensor data fusion 211

9.2 Expression of the Bayesian filter 218

9.3 Signal discrimination process 221

9.4 Extensions of the basic MSF 228

9.5 Examples of applications 232

Conclusion 241

Bibliography 249

Index 257

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