Distributed Inference for Relay-Assisted Sensor Networks With Intermittent Measurements Over Fading Channels

TL;DR

This paper introduces a fully distributed estimation algorithm for relay-assisted sensor networks that handles intermittent measurements and fading channels, ensuring unbiasedness and consistency without a central fusion center.

Contribution

It proposes a novel innovation-based distributed estimation algorithm that manages asymmetric, time-varying communications and provides theoretical guarantees for its performance.

Findings

Algorithm achieves asymptotic unbiasedness and consistency.

Simulation results validate theoretical analysis.

Design tailored for energy-constrained networks.

Abstract

In this paper, we consider a general distributed estimation problem in relay-assisted sensor networks by taking into account time-varying asymmetric communications, fading channels and intermittent measurements. Motivated by centralized filtering algorithms, we propose a distributed innovation-based estimation algorithm by combining the measurement innovation (assimilation of new measurement) and local data innovation (incorporation of neighboring data). Our algorithm is fully distributed which does not need a fusion center. We establish theoretical results regarding asymptotic unbiasedness and consistency of the proposed algorithm. Specifically, in order to cope with time-varying asymmetric communications, we utilize an ordering technique and the generalized Perron complement to manipulate the first and second moment analyses in a tractable framework. Furthermore, we present a performance-oriented design of the proposed algorithm for energy-constrained networks based on the theoretical results. Simulation results corroborate the theoretical findings, thus demonstrating the effectiveness of the proposed algorithm.