Detection of known signals under a weakly dependent noise model

Abstract

In this thesis we consider the discrete-time signal detection problem under the presence of additive noise exhibiting weak dependence. We first propose a weakly dependent noise model, in which the additive noise is modeled as a moving average process. We derive the locally optimum, memoryless, and one-memory detector test statistics under the model. The asymptotic performance of the one-memory detector is compared with that of the memoryless, linear correlator, and sign correlator detectors. Specific examples for the asymptotic performance comparison of these detectors are considered. We also investigate the finite sample-size performance of several detectors through the Monte-Carlo simulation. We observe that the one-memory detector can achieve almost optimum performance at the expense of only one memory unit under the weakly dependent noise model and is rather insensitive to slight model change.