Distributed Detection of a Non-cooperative Target via Generalized Locally-optimum Approaches

TL;DR

This paper develops and compares distributed detection methods for a non-cooperative target in wireless sensor networks, focusing on reducing computational complexity while maintaining detection performance.

Contribution

It introduces generalized locally-optimum detection rules for distributed target detection, addressing computational challenges in complex sensor network scenarios.

Findings

Proposed fusion rules outperform traditional methods in accuracy.

Generalized LOD rules reduce computational complexity.

All methods are evaluated and compared in terms of performance and efficiency.

Abstract

In this paper we tackle distributed detection of a non-cooperative target with a Wireless Sensor Network (WSN). When the target is present, sensors observe an unknown random signal with amplitude attenuation depending on the distance between the sensor and the target (unknown) positions, embedded in white Gaussian noise. The Fusion Center (FC) receives sensors decisions through error-prone Binary Symmetric Channels (BSCs) and is in charge of performing a (potentially) more-accurate global decision. The resulting problem is a one-sided testing with nuisance parameters present only under the target-present hypothesis. We first focus on fusion rules based on Generalized Likelihood Ratio Test (GLRT), Bayesian and hybrid approaches. Then, aimed at reducing the computational complexity, we develop fusion rules based on generalizations of the well-known Locally-Optimum Detection (LOD) framework. Finally, all the proposed rules are compared in terms of performance and complexity.