Threshold detection in narrowband non-Gaussian noise

TLDR: It is shown that this noise model can itself be closely approximated by a computationally much simpler noise model, and the simplest form of this approximation yields nearly optimal (asymptotic) performance for locally optimum detection.

Abstract: : The Middleton Class A narrowband non-Gaussian noise model is examined. It is shown that this noise model (which is known to fit closely a variety of non-Gaussian noises) can itself be closely approximated by a computationally much simpler noise model. It is then shown by numerical examples that, for the problem of locally optimum detection, the simplest form of this approximation yields nearly optimal (asymptotic) performance. The performance of other simple suboptimal threshold detectors in Class A noise is also examined. Finally, a useful relationship between the Class A model and the epsilon-mixture model is developed. (Author)