Adaptive distributed compressed estimation based on recursive least squares with sensing matrix design

Abstract

In this paper, a distributed compressed estimation (DCE) scheme is presented based on a distributed recursive-least squares algorithm for sparse signals and systems along with a sensing matrix design procedure based on compressive sensing techniques. The D-CE scheme consists of compression and decompression modules inspired by compressive sensing to perform distributed compressed estimation. A design procedure is developed under the DCE framework and a novel algorithm is developed to optimize the sensing matrix, which can further improve the performance of the proposed DCE and distributed adaptive algorithms. Simulations for a wireless sensor network show the advantages of the proposed scheme and algorithm in terms of convergence rate and mean square error performance.