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

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.

To appear in IEEE Signal Processing Letters

References in corpus (1)

• Optimality of Received Energy in Decision Fusion over Rayleigh Fading Diversity MAC with Non-Identical Sensors

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