DocumentCode :
109876
Title :
A Contraction Mapping Approach for Robust Estimation of Lagged Autocorrelation
Author :
Seelamantula, Chandra Sekhar ; Shenoy, Ravi R.
Author_Institution :
Dept. of Electr. Eng., Indian Inst. of Sci., Bangalore, India
Volume :
21
Issue :
9
fYear :
2014
fDate :
Sept. 2014
Firstpage :
1054
Lastpage :
1058
Abstract :
We consider the zero-crossing rate (ZCR) of a Gaussian process and establish a property relating the lagged ZCR (LZCR) to the corresponding normalized autocorrelation function. This is a generalization of Kedem´s result for the lag-one case. For the specific case of a sinusoid in white Gaussian noise, we use the higher-order property between lagged ZCR and higher-lag autocorrelation to develop an iterative higher-order autoregressive filtering scheme, which stabilizes the ZCR and consequently provide robust estimates of the lagged autocorrelation. Simulation results show that the autocorrelation estimates converge in about 20 to 40 iterations even for low signal-to-noise ratio.
Keywords :
Gaussian noise; autoregressive processes; correlation theory; estimation theory; filtering theory; iterative methods; signal denoising; Gaussian process; contraction mapping approach; iterative higher order autoregressive filtering scheme; lagged autocorrelation; normalized autocorrelation function; robust estimation; white Gaussian noise; zero crossing rate; Correlation; Estimation; Gaussian noise; Gaussian processes; Indexes; Robustness; Contraction mapping; frequency estimation; lagged ZCR; lagged autocorrelation; zero-crossing rate (ZCR);
fLanguage :
English
Journal_Title :
Signal Processing Letters, IEEE
Publisher :
ieee
ISSN :
1070-9908
Type :
jour
DOI :
10.1109/LSP.2014.2322588
Filename :
6812125
Link To Document :
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