Distributed and Recursive Parameter Estimation in Parametrized Linear State-Space Models

Abstract

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> We consider a network of sensors deployed to sense a spatio-temporal field and infer parameters of interest about the field. We are interested in the case where each sensor's observation sequence is modeled as a state-space process that is perturbed by random noise, and the models across sensors are parametrized by the <emphasis emphasistype="italic">same</emphasis> parameter vector. The sensors collaborate to estimate this parameter from their measurements, and to this end we propose a distributed and recursive estimation algorithm, which we refer to as the incremental recursive prediction error algorithm. This algorithm has the distributed property of incremental gradient algorithms and the on-line property of recursive prediction error algorithms. </para>