Thresholding strategy in passive detection via wavelet filter banks

In this paper, we evaluate thresholding algorithms applied in wavelet filter bank (FB) based passive detection in radar and sonar systems. Addressing a random emission with unknown parameters, the emission and the received signal are modelled by a Gaussian process with unknown mean and variance. The wavelet FB decomposes the received signal into subchannels. A decision on the absence and presence of the emission is then made by thresholding against the FB outputs. Formulae are derived to prove the detection performance using different thresholding strategies. A graphical comparison of the detection performances with different strategies and in different scenarios is presented. Design principles are derived under this wavelet FB based detection framework.