Channel-Aware Decision Fusion with Rao Test for Multisensor Fusion

This paper tackles unknown signal detection in a distributed fashion via a Wireless Sensor Network (WSN) made of tiny and low-cost sensor devices. The sensors are assumed to measure an unknown deterministic parameter within unimodal and symmetric noise. Since usual Internet of Things (IoT) scenarios require energy-constrained operations, one-bit quantization of the raw measurement is locally performed at each sensor. A Fusion Center (FC) receives noisy quantized sensor observations through reporting parallel-access Rayleigh channels and makes a global decision. We propose the Rao test as a simpler alternative to the Generalized Likelihood Ratio Test (GLRT) for multisensor fusion. The intent of our work is performing fusion directly from the received signals, following a decode-and-fuse approach. Then, we study the design of the (channel-aware) quantizer of each sensor with the intent of maximizing the asymptotic detection probability. Finally, we compare the performance of the Rao test with that of the GLRT by simulations (related to a practical WSN scenario).