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