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

Author

Ijima, Hiroshi ; Yamashita, Yukinori ; Ohsumi, Akira

Author_Institution

Wakayama Univ., Wakayama

fYear

2007

fDate

15-18 Dec. 2007

Firstpage

1060

Lastpage

1064

Abstract

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.

Keywords

Kalman filters; differential equations; random noise; signal detection; stochastic processes; Kalman filter; incorporated data stationarization; nonstationary random noise; signal detection; stochastic differential equation; Adaptive signal detection; Background noise; Differential equations; Indium tin oxide; Parameter estimation; Signal detection; Signal processing; Stochastic resonance; Testing; Working environment noise; Kalman filter; Nonstationary random noise; Signal detection; Time-varying ARMA model;

fLanguage

English

Publisher

ieee

Conference_Titel

Signal Processing and Information Technology, 2007 IEEE International Symposium on

Conference_Location

Giza

Print_ISBN

978-1-4244-1835-0

Electronic_ISBN

978-1-4244-1835-0

Type

conf

DOI

10.1109/ISSPIT.2007.4458098

Filename

4458098