Detection of Signals in Nonstationary Random Noise via Stationarization of Data Incorporated with Kalman Filter

Recently, the authors have proposed a method for the detection of signals corrupted by nonstationary random noise based on stationarization of the observation data which can be modeled by the first-order Ito stochastic differential equation. In this paper, in order to apply this method to more general situation, we propose a stationarization method incorporated with Kalman filter. To test the proposed method simulation experiments are presented.