Hard decision based distributed detection in multi-sensor system over noise correlated sensing channels

In spite of the spectrum sensing importance, small part of the literature deals with the case of dependency between sensors' observations. In this paper, Bayes criterion for minimizing the probability of error (Pe) is used to find the distributed decision rule at each sensor in a binary hypothesis multi-sensor system, while considering the correlation over the sensing channels. This paper proposes a new, simple, yet efficient algorithm to find the local decision rules to achieve the objective of minimizing the Pe. Upon receiving the signal, each sensor sends a local hard decision to the fusion center (FC). The FC then generates a global decision using the K-out-of-N majority rule. The derived problem formulation and the proposed algorithm can be used with any joint probability density functions and with any odd number of sensors. The proposed framework also accounts for the path attenuation effect on the global decision. The results show the effectiveness and validity of the proposed framework in practical sensor systems.