# Generalized Rao Test for Decentralized Detection of an Uncooperative Target

## TL;DR

This paper develops a novel decentralized detection method using a Generalized Rao test for identifying an uncooperative target in a wireless sensor network, addressing unknown target location and noisy communication channels.

## Contribution

It introduces a new fusion rule based on the G-Rao test, reducing computational complexity in decentralized detection with nuisance parameters.

## Key findings

- G-Rao test outperforms GLRT in computational efficiency
- Proposed threshold-optimization improves detection accuracy
- Simulation results confirm the effectiveness of the new fusion rule

## Abstract

We tackle distributed detection of a non-cooperative target with a Wireless Sensor Network (WSN). When the target is present, sensors observe an (unknown) deterministic signal with attenuation depending on the distance between the sensor and the (unknown) target positions, embedded in symmetricand unimodal noise. The Fusion Center (FC) receives quantized sensor observations through error-prone Binary Symmetric Channels (BSCs) and is in charge of performing a more-accurate global decision. The resulting problem is a two-sided parameter testing with nuisance parameters (i.e. the target position) present only under the alternative hypothesis. After introducing the Generalized Likelihood Ratio Test (GLRT) for the problem, we develop a novel fusion rule corresponding to a Generalized Rao (G-Rao) test, based on Davies' framework, to reduce the computational complexity. Also, a rationale for threshold-optimization is proposed and confirmed by simulations. Finally, the aforementioned rules are compared in terms of performance and computational complexity.