Multisensor Fusion Estimation Theory and Application

Preface
Acknowledgements
Contents
Introduction to Optimal Fusion Estimation and Kalman Filtering: Preliminaries
1 Introduction to Optimal Fusion Estimation
	1.1 Definition of Multisensor Data Fusion
	1.2 The Principle and Architecture of Multi-sensor Data Fusion
		1.2.1 Detection Level Fusion
		1.2.2 Position Level Fusion
		1.2.3 Attribute Level Fusion/Target Recognition Level Fusion
		1.2.4 Situation Assessment and Threat Assessment
	1.3 Advantages and Disadvantages for Multisensor Data Fusion
	1.4 Conclusion
	References
2 Kalman Filtering of Discrete Dynamic Systems
	2.1 Overview of the Discrete-Time Kalman Filter
		2.1.1 Prediction
		2.1.2 Update
		2.1.3 Alternate Forms of Updated Covariance and Kalman Gain
	2.2 Properties of the Kalman Filter
	2.3 Alternate Propagation of Covariance
		2.3.1 Multiple State Systems
		2.3.2 Divergence Issues
	2.4 Sequential Kalman Filtering
	2.5 Information Filtering
	2.6 Summary
	References
State Fusion Estimation for Networked Systems
3 Fusion Estimation for Linear Systems with Cross-Correlated Sensor Noises
	3.1 Introduction
	3.2 Problem Formulation
	3.3 Linear Transformation
	3.4 The Optimal State Fusion Estimation Algorithms
		3.4.1 The Centralized State Fusion Estimation with Raw Data
		3.4.2 The Centralized Fusion with Transformed Data
		3.4.3 The Optimal State Estimation by Distributed Fusion
		3.4.4 The Complexity Analysis
	3.5 Numerical Example
	3.6 Summary
	References
4 Distributed Data Fusion for Multirate Sensor Networks
	4.1 Introduction
	4.2 Problem Formulation
	4.3 The Data Fusion Algorithms for State Estimation
		4.3.1 The Centralized Fusion
		4.3.2 The Sequential Fusion
		4.3.3 Two-Stage Distributed Fusion
	4.4 Numerical Example
	4.5 Summary
	References
5 State Estimation for Multirate Systems with Unreliable Measurements
	5.1 Introduction
	5.2 Problem Formulation
	5.3 The Sequential Fusion Algorithm
	5.4 Numerical Example
	5.5 Conclusions
	References
6 Distributed Fusion Estimation for Systems with Network Delays and Uncertainties
	6.1 Introduction
	6.2 Model and Problem Statements
	6.3 Optimal Local Kalman Filter Estimator with a Buffer of Finite Length
	6.4 Distributed Weighted Kalman Filter Fusion with Buffers of Finite Length
	6.5 Simulation Results
	6.6 Conclusion
	References
7 State Estimation of Asynchronous Multirate Multisensor Systems
	7.1 Introduction
	7.2 Problem Formulation
	7.3 The Optimal State Fusion Estimation Algorithm
		7.3.1 Modeling of Asynchronous, Multirate, Multisensor Systems
		7.3.2 Data Fusion with Normal Measurements
		7.3.3 Data Fusion with Unreliable Measurements
	7.4 Numerical Example
	7.5 Summary
	References
Fusion Estimation Under Event-Triggered Mechanisms
8 Event-Triggered Centralized Fusion for Correlated Noise Systems
	8.1 Introduction
	8.2 Problem Formulation
		8.2.1 System Model Characterization
		8.2.2 Event-Triggered Mechanism of Sensors
	8.3 The State Fusion Estimation Algorithm with Event-Triggered Mechanism
		8.3.1 Event-Triggered Kalman Filter with Correlated Noise
		8.3.2 Batch Fusion Algorithm with Correlated Noise
	8.4 Numerical Example
	8.5 Conclusions
	References
9 Event-Triggered Distributed Fusion Estimation for WSN Systems
	9.1 Introduction
	9.2 Problem Formulation
		9.2.1 System Model Characterization
		9.2.2 Event-Triggered Mechanism of Sensors
	9.3 Fusion Algorithm with Event-Triggered Mechanism
		9.3.1 Kalman Filter with Event-Triggered Mechanism
		9.3.2 Distributed Fusion Algorithm in WSNs
	9.4 Numerical Example
	9.5 Conclusions
	References
10 Event-Triggered Sequential Fusion for Systems with Correlated Noises
	10.1 Introduction
	10.2 Problem Formulation
		10.2.1 System Modeling
		10.2.2 Event–Triggered Mechanism of Sensors
	10.3 Fusion Algorithm with Event–Triggered Mechanism
		10.3.1 Event–Triggered Kalman Filter with Correlated Noises
		10.3.2 Event–Triggered Sequential Fusion Estimation Algorithm with Correlated Noises
	10.4 Boundness of the Fusion Estimation Error Covariance
	10.5 Numerical Example
	10.6 Conclusions
	References
Fusion Estimation for Systems with Heavy-Tailed Noises
11 Distributed Fusion Estimation for Multisensor Systems with Heavy-Tailed Noises
	11.1 Introduction
	11.2 Problem Formulation
	11.3 Linear Filter and Information Filter for Systems with Heavy-Tailed Noises
	11.4 The Information Fusion Algorithms
		11.4.1 The Centralized Batch Fusion
		11.4.2 The Distributed Fusion Algorithms
	11.5 Simulation
	11.6 Conclusions
	References
12 Sequential Fusion Estimation for Multisensor Systems with Heavy–Tailed Noises
	12.1 Introduction
	12.2 Problem Formulation
	12.3 The Sequential Fusion Algorithm
	12.4 Numerical Example
	12.5 Conclusion
	References