Distributed and Recursive Estimation

Estimation is a canonical problem in sensor networks. The intrinsic nature of sensor networks requires estimation algorithms based on sensor data to be distributed and recursive; such algorithms are studied in this chapter for the problem of (conditional) least squares estimation. The chapter is divided into three parts. In the first part, distributed and recursive estimation algorithms are developed for the nonlinear regression problem. In the second part, a distributed and recursive algorithm is designed to estimate the unknown parameter in a parametrized state-space random process. In the third part, the problem of identifying the source of a diffusion field is discussed as a representative application for the algorithms developed in the first two parts.