Rank Correlation Based Detection of Known Signals in Middleton's Class-A Noise

This letter proposes to apply two rank correlations, namely, Kendall's tau (KT) and Spearman's rho (SR), to the fundamental problem of detecting known signals in impulsive noise. Under the popular Middleton's Class-A impulsive noise model, we derived the expectations and variances of KT and SR, which, along with the central limit theorem, are further employed to determing the detection threshold and the detection probability. Monte Carlo simulations not only verified the correctness of our theoretical findings, but also demonstrated the superiority of KT and SR to other five state-of-the-art methods in terms of detection probability.