COMPARISON OF KNOWN SIGNAL-DETECTION SCHEMES UNDER A WEAKLY DEPENDENTNOISE MODEL

The authors consider the discrete-time signal detection problem under the presence of additive noise exhibiting weak dependence. They first propose a weakly dependent noise model, in which the additive noise is modelled as a moving average process. They derive the locally optimum , memoryless, and one-memory detector test statistics under the model. The asymptotic performance of the one-memory detector is compared wit h that of the locally optimum and memoryless detectors. Specific examp les for the asymptotic performance comparison of these detectors are c onsidered. The authors also investigate the finite sample-size perform ance of several detectors through Monte-Carlo simulation. It is observ ed that the one-memory detector can achieve almost optimum performance at the expense of only one memory unit under the weakly dependent noi se model, and is rather insensitive to slight model change.