Noise Variance Estimation In Signal Processing
- Creators
- Makovoz, David
Abstract
We present a new method of estimating noise variance. The method is applicable for 1D and 2D signal processing. The essence of this method is estimation of the scatter of normally distributed data with high level of outliers. The method is applicable to data with the majority of the data points having no signal present. The method is based on the shortest half sample method. The mean of the shortest half sample (shorth) and the location of the least median of squares are among the most robust measures of the location of the mode. The length of the shortest half sample has been used as the measurement of the data scatter of uncontaminated data. We show that computing the length of several sub samples of varying sizes provides the necessary information to estimate both the scatter and the number of uncontaminated data points in a sample. We derive the system of equations to solve for the data scatter and the number of uncontaminated data points for the Gaussian distribution. The data scatter is the measure of the noise variance. The method can be extended to other distributions.
Additional Information
© 2006 IEEE. Issue Date: Aug. 2006. Date of Current Version: 29 January 2007.Attached Files
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Additional details
- Eprint ID
- 22518
- Resolver ID
- CaltechAUTHORS:20110225-102806827
- Created
-
2011-02-25Created from EPrint's datestamp field
- Updated
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2021-11-09Created from EPrint's last_modified field
- Other Numbering System Name
- INSPEC Accession Number
- Other Numbering System Identifier
- 9387924