Locally Optimum Detection of Signals in Multiplicative and First-Order Markov Additive Noises

In most previously reported studies on locally optimum detection of signals, independent observations have been assumed in various noise environments. The use of an independent observation model may cause a considerable performance degradation in detection applications of modern high data-rate communication systems exhibiting dependence among interference components. In this paper, the detection of weak known and random signals is addressed in observations corrupted by multiplicative and first-order Markov additive noises. The asymptotic and finite sample-size performance of several detectors are obtained and compared: it is confirmed that the dependence of noise components need to be taken into account to maintain detection performance appropriately.