Distributed decision fusion under nonideal communication channels with adaptive topology

Multi-sensor decision fusion has attracted some attention in information fusion field, meanwhile, the distributed target detection has been a well-studied topic in the multi-sensor detection theory. This paper investigates the increase in detection reliability that an adaptive network (with adaptive topologies and nonideal channels and decision fusion rules) can provide, compared with a fixed topology network. We consider a network, consisting of K-local uncertainty sensors and a Fusion Center (FC) tasked with detecting the presence or absence of a target in the Region of Interest (ROI). Sensors transmit binary modulated local decisions over nonideal channels modeled as Gaussian noise or fading channels. Assuming that the signal intensity emitted by a target follows the isotropic attenuation power model, we consider three classes of network topology architectures: (1) serial topology; (2) tree topology, and (3) parallel topology. Under the Neyman-Pearson (NP) criterion, we derive the optimal threshold fusion rule with adaptive topology to minimize the error probability. Extensive simulations are conducted to validate the correctness and effectiveness of the proposed algorithms.