ABSTRACT
This chapter examines the problem of distributed state estimation for two kinds of important systems: the plant wide processes and the sensor networks. Then it introduces a distributed filtering scheme that allows the nodes of a sensor network to track the average of n sensor measurements using an average consensus-based distributed filter called a consensus filter. This consensus filter plays a crucial role in solving a data fusion problem that allows implementation of a scheme for distributed Kalman filtering in sensor networks. Next, we develop a systematic procedure leading to a distributed estimation in WSN using the extended information filter for target tracking and the adaptive sensor selection (ASS) algorithm. The algorithm incorporates a cost function based on the geometrical dilution of precision (GDOP) for sensor selection. Finally, multi-sensor management is formally described as a system or process that seeks to manage or coordinate the usage of a suite of sensors or measurement devices in a dynamic, uncertain environment, to improve the performance of data fusion, and ultimately that of perception.
