Title
Detection of signals corrupted by nonstationary random noise via Kalman filter-based stationarization approach
Abstract
In this paper, a method of stationarization of nonstationary data is proposed in the signal detection problem. The signal to be detected is corrupted in a nonstationary random noise whose model is given by an ARMA(p, q) model. The time-varying coefficient parameters of the ARMA model are estimated by the Kalman filter. The stationalization of nonstationary observation data based on the estimated coefficient parameters leads us to the conventional binary hypothesis-testing for signals in stationary random noise.
Year
Venue
Keywords
2008
Lausanne
kalman filters,autoregressive moving average processes,random noise,signal denoising,signal detection,arma model,kalman filter,binary hypothesis-testing,nonstationary data,nonstationary observation data,nonstationary random noise,signal detection problem,stationarization approach,time-varying coefficient parameters
Field
DocType
ISSN
Autoregressive–moving-average model,Detection theory,Computer science,Random noise,Algorithm,Speech recognition,Moving horizon estimation,Kalman filter,Binary number
Conference
2219-5491
Citations 
PageRank 
References 
0
0.34
3
Authors
2
Name
Order
Citations
PageRank
Hiroshi Ijima100.34
Akira Ohsumi2173.44