Distributed detection of Rayleigh target in K-Distributed clutter

Detection of target in the presence of Gaussian clutter using multiple radars have been studied in the recent literature. The algorithms considered include pure decision based fusion rules such as OR and AND and tests involving the fusion of partial information, such as normalized test statistic (NTS). However, based on experimental evidence, in many situations, the compound K-distributed model seems to be a good fit for the clutter envelope. In this paper we study through simulation the performances of the NTS, AND, and OR tests for the detection of Rayleigh target in the presence of K-distributed clutter. The results show that for large signal to clutter power ratio and for large shape parameter values, the K-NTS significantly outperforms both the OR and the AND rules.