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**Authors: **Yunmin Zhu **ISBN-13: ****9781402072581**, **ISBN-10: ****1402072589**
**Format: **Hardcover **Publisher: **Springer-Verlag New York, LLC
**Date Published: **November 2007 **Edition: **(Non-applicable)

List of Figures | ||

List of Tables | ||

Preface | ||

Acknowledgments | ||

1 | Introduction | 3 |

1.1 | Conventional Statistical Decision | 3 |

1.2 | Multisensor Statistical Decision Fusion Summary | 6 |

1.3 | Three Conventional Single Sensor Decisions | 11 |

2 | Two Sensor Binary Decisions | 37 |

2.1 | Introduction | 37 |

2.2 | Optimal Sensor Rule of Bayes Decision | 41 |

2.3 | An Algorithm for Computing the Optimal Sensor Rule | 48 |

2.4 | Relationships with Likelihood Ratio Sensor Rules | 53 |

2.5 | Numerical Examples | 55 |

2.6 | Randomized Fusion Rules | 60 |

3 | Multisensor Binary Decisions | 63 |

3.1 | The Formulation for Bayes Binary Decision Problem | 64 |

3.2 | Formulation of Fusion Rules via Polynomials of Sensor Rules | 65 |

3.3 | Fixed Point Type Necessary Condition for the Optimal Sensor Rules Given a Fusion Rule | 67 |

3.4 | The Finite Convergence of the Discretized Algorithm | 71 |

3.5 | The Optimal Fusion and Some Interesting Properties | 78 |

3.6 | Numerical Examples of the Above Results | 83 |

3.7 | Optimal Sensor Rule of Neyman-Pearson Decision | 88 |

3.8 | Sequential Decision Fusion Given Fusion Rule | 94 |

4 | Multisensor Multi-Hypothesis Network Decision | 101 |

4.1 | Elementary Network Structures | 101 |

4.2 | Formulation of Fusion Rule via Polynomial of Sensor rules | 106 |

4.3 | Fixed Point Type Necessary Condition for Optimal Sensor Rules Given a Fusion Rule | 110 |

4.4 | Iterative Algorithm and Convergence | 112 |

5 | Optimal Fusion Rule and Design of Network Communication Structures | 117 |

5.1 | Optimal Fusion Rule Given Sensor Rules | 117 |

5.2 | The Equivalent Classes of Fusion Rules | 134 |

5.3 | Unified Fusion Rule for Parallel Network | 140 |

5.4 | Unified Fusion Rule for Tandem and Tree Networks | 145 |

5.5 | Performance Comparison of Parallel and Tandem Networks | 146 |

5.6 | Numerical Examples | 148 |

5.7 | Optimization Design of Network Decision Systems | 153 |

6 | Multisensor Point Estimation Fusion | 159 |

6.1 | Previous Main Results | 160 |

6.2 | Linear Minimum Variance Estimation Fusion | 162 |

6.3 | The Optimality of Kalman Filtering Fusion with Feedback | 177 |

6.4 | Fusion of the Forgetting Factor RLS Algorithm | 184 |

7 | Multisensor Interval Estimation Fusion | 197 |

7.1 | Statistical Interval Estimation Fusion Using Sensor Statistics | 198 |

7.2 | Interval Estimation Fusion Using Sensor Estimates | 212 |

7.3 | Fault-Tolerant Interval Estimation Fusion | 219 |

Index | 235 |

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