Networked Filtering and Fusion in Wireless Sensor Networks

By exploiting the synergies among available data, information fusion can reduce data traffic, filter noisy measurements, and make predictions and inferences about a monitored entity. Networked Filtering and Fusion in Wireless Sensor Networks introduces the subject of multi-sensor fusion as the method of choice for implementing distributed systems.

The book examines the state of the art in information fusion. It presents the known methods, algorithms, architectures, and models of information fusion and discusses their applicability in the context of wireless sensor networks (WSNs). Paying particular attention to the wide range of topics that have been covered in recent literature, the text presents the results of a number of typical case studies.

Complete with research supported elements and comprehensive references, this teaching-oriented volume uses standard scientific terminology, conventions, and notations throughout. It applies recently developed convex optimization theory and highly efficient algorithms in estimation fusion to open up discussion and provide researchers with an ideal starting point for further research on distributed estimation and fusion for WSNs.

The book supplies a cohesive overview of the key results of theory and applications of information-fusion-related problems in networked systems in a unified framework. Providing advanced mathematical treatment of fundamental problems with information fusion, it will help you broaden your understanding of prospective applications and how to address such problems in practice.

After reading the book, you will gain the understanding required to model parts of dynamic systems and use those models to develop distributed fusion control algorithms that are based on feedback control theory.

Table of Contents

Chapter 1: Introduction
Chapter 2: Wireless Sensor Networks
Chapter 3: Distributed Sensor Fusion
Chapter 4: Distributed Kalman Filtering
Chapter 5: Expectation Maximization
Chapter 6: Wireless Estimation Methods
Chapter 7: Multi-Sensor Fault Estimation
Chapter 8: Multi-Sensor Data Fusion
Chapter 9: Approximate Distributed Estimation
Chapter 10: Estimation via Information Matrix
Chapter 11: Filtering in Sensor Networks