This book focuses on the basic theory and methods of multisensor data fusion state estimation and its application. It consists of four parts with 12 chapters. In Part I, the basic framework and methods of multisensor optimal estimation and the basic concepts of Kalman filtering are briefly and systematically introduced. In Part II, the data fusion state estimation algorithms under networked environment are introduced. Part III consists of three chapters, in which the fusion estimation algorithms under event-triggered mechanisms are introduced. Part IV consists of two chapters, in which fusion estimation for systems with non-Gaussian but heavy-tailed noises are introduced. The book is primarily intended for researchers and engineers in the field of data fusion and state estimation. It also benefits for both graduate and undergraduate students who are interested in target tracking, navigation, networked control, etc.
Author(s): Liping Yan, Lu Jiang, Yuanqing Xia
Publisher: Springer Singapore
Year: 2020
Language: English
Pages: 227
City: Singapore
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
