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

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3 Author(s)
Ijima, Hiroshi ; Wakayama Univ., Wakayama ; Yamashita, Y. ; Ohsumi, A.

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.

Published in:

Signal Processing and Information Technology, 2007 IEEE International Symposium on

Date of Conference:

15-18 Dec. 2007