Maximum-entropy Surrogation in Network Signal Detection

TL;DR

This paper introduces a maximum-entropy method to estimate missing measurements in sensor networks, enabling detection in partially connected networks and highlighting the broader use of maximum-entropy baselines for information quantification.

Contribution

It proposes a novel maximum-entropy surrogation technique for missing data in network signal detection, extending applicability to non-complete network graphs.

Findings

Enables detection in networks with incomplete connectivity.

Provides a framework for quantifying information value using maximum-entropy.

Shows potential for improved sensor network data analysis.

Abstract

Multiple-channel detection is considered in the context of a sensor network where raw data are shared only by nodes that have a common edge in the network graph. Established multiple-channel detectors, such as those based on generalized coherence or multiple coherence, use pairwise measurements from every pair of sensors in the network and are thus directly applicable only to networks whose graphs are completely connected. An approach introduced here uses a maximum-entropy technique to formulate surrogate values for missing measurements corresponding to pairs of nodes that do not share an edge in the network graph. The broader potential merit of maximum-entropy baselines in quantifying the value of information in sensor network applications is also noted.