One-bit Decentralized Detection with a Rao Test for Multisensor Fusion

TL;DR

This paper introduces the Rao test as a simpler alternative to the GLRT for multisensor fusion in decentralized detection, analyzing its performance and optimal quantization in noisy binary channels.

Contribution

The paper proposes using the Rao test for multisensor fusion, providing analysis of optimal quantizer thresholds and asymptotic performance, with theoretical and simulation validation.

Findings

Rao test performs comparably to GLRT in multisensor fusion scenarios.

Optimal quantizer thresholds are derived for error-prone binary channels.

Asymptotic performance of the Rao test is characterized for homogeneous sensors.

Abstract

In this letter we propose the Rao test as a simpler alternative to the generalized likelihood ratio test (GLRT) for multisensor fusion. We consider sensors observing an unknown deterministic parameter with symmetric and unimodal noise. A decision fusion center (DFC) receives quantized sensor observations through error-prone binary symmetric channels and makes a global decision. We analyze the optimal quantizer thresholds and we study the performance of the Rao test in comparison to the GLRT. Also, a theoretical comparison is made and asymptotic performance is derived in a scenario with homogeneous sensors. All the results are confirmed through simulations.