DocumentCode
3135909
Title
Sinusoidal signal detection using the minimum description length and the predictive stochastic complexity
Author
Valaee, S. ; Champagne, B. ; Kabal, P.
Author_Institution
Dept. of Electr. Eng., Tarbiat Modares Univ., Tehran, Iran
Volume
2
fYear
1997
fDate
2-4 Jul 1997
Firstpage
1023
Abstract
Two techniques based on the minimum description length (MDL) and the predictive stochastic complexity (PSC) are proposed for sinusoidal signal detection. The MDL and PSC criteria are the codelength of the observation and the model. The proposed techniques decompose the observation vector into its components in the signal and noise subspaces. The noise component is encoded for several model orders. The best model is selected by minimizing the codelength
Keywords
encoding; prediction theory; signal detection; stochastic processes; time series; codelength; encoding; minimum description length; model orders; noise component; observation vector; predictive stochastic complexity; sinusoidal signal detection; Frequency; Probability density function; Signal detection; Signal generators; Stochastic processes; Tiles; White noise;
fLanguage
English
Publisher
ieee
Conference_Titel
Digital Signal Processing Proceedings, 1997. DSP 97., 1997 13th International Conference on
Conference_Location
Santorini
Print_ISBN
0-7803-4137-6
Type
conf
DOI
10.1109/ICDSP.1997.628538
Filename
628538
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