Distributed Compressed Estimation for Wireless Sensor Networks Based on Compressive Sensing

TL;DR

This paper introduces a new distributed compressed estimation method for wireless sensor networks that leverages compressive sensing, optimizing measurement matrices to enhance convergence and accuracy.

Contribution

It presents a novel distributed estimation scheme based on compressive sensing with an optimized measurement matrix design for wireless sensor networks.

Findings

Improved convergence rate in simulations

Reduced mean square error performance

Effective distributed estimation for sparse signals

Abstract

This letter proposes a novel distributed compressed estimation scheme for sparse signals and systems based on compressive sensing techniques. The proposed scheme consists of compression and decompression modules inspired by compressive sensing to perform distributed compressed estimation. A design procedure is also presented and an algorithm is developed to optimize measurement matrices, which can further improve the performance of the proposed distributed compressed estimation scheme. Simulations for a wireless sensor network illustrate the advantages of the proposed scheme and algorithm in terms of convergence rate and mean square error performance.