A Reliability of Measurement Based Algorithm for Adaptive Estimation in Sensor Networks

TL;DR

This paper introduces a new adaptive estimation algorithm for sensor networks that accounts for measurement reliability by estimating sensor noise variances and adjusting step sizes accordingly, improving estimation accuracy.

Contribution

It proposes a novel two-phase method that estimates sensor noise variances and adapts the IDLMS algorithm for enhanced reliability in distributed estimation.

Findings

Significant performance improvement over standard IDLMS

Effective noise variance estimation for sensors

Adaptive step-size adjustment enhances accuracy

Abstract

In this paper we consider the issue of reliability of measurements in distributed adaptive estimation problem. To this aim, we assume a sensor network with different observation noise variance among the sensors and propose new estimation method based on incremental distributed least mean-square (IDLMS) algorithm. The proposed method contains two phases: I) Estimation of each sensors observation noise variance, and II) Estimation of the desired parameter using the estimated observation variances. To deal with the reliability of measurements, in the second phase of the proposed algorithm, the step-size parameter is adjusted for each sensor according to its observation noise variance. As our simulation results show, the proposed algorithm considerably improves the performance of the IDLMS algorithm in the same condition.