In this paper a Kalman filter-based detector is developed for signals which are corrupted by nonstationary random noise. The signal detection problem is investigated using the stationarization approach to nonstationary data. The model of the corrupting noise is given by an ARMA(p,q) model with unknown time-varying coefficients. These coefficient parameters are estimated from the (original) observation data by the Kalman filter.
