Finite sample-size performance of known signal detectors in a weakly dependent noise model

In this paper 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. We investigate the finite sample-size performance of several detectors through 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