Paper Info
Title
Robust Savitzky-Golay filters
Abstract
Local polynomial approximation of data is an approach towards signal denoising. Savitzky-Golay (SG) filters are finite-impulse-response kernels, which convolve with the data to result in polynomial approximation for a chosen set of filter parameters. In the case of noise following Gaussian statistics, minimization of mean-squared error (MSE) between noisy signal and its polynomial approximation is optimum in the maximum-likelihood (ML) sense but the MSE criterion is not optimal for non-Gaussian noise conditions. In this paper, we robustify the SG filter for applications involving noise following a heavy-tailed distribution. The optimal filtering criterion is achieved by ℓ1-norm minimization of error through iteratively reweighted least-squares (IRLS) technique. It is interesting to note that at any stage of the iteration, we solve a weighted SG filter by minimizing ℓ2 norm but the process converges to ℓ1 minimized output. The results show consistent improvement over the standard SG filter performance.
Year
DOI
Venue
2014
10.1109/ICDSP.2014.6900752
Digital Signal Processing
Keywords
DocType
ISSN
FIR filters,Gaussian distribution,Gaussian noise,least mean squares methods,maximum likelihood estimation,minimisation,polynomial approximation,signal denoising,Gaussian statistics,IRLS technique,ML,MSE criterion,SG filter,finite-impulse-response kernel,iteratively reweighted least-squares technique,maximum-likelihood sense,mean-squared error minimization,nonGaussian noise condition,optimal filtering criterion,polynomial approximation,robust Savitzky-Golay filters,signal denoising,Finite impulse response,Iteratively reweighted least-squares technique,Mean-squared error,Savitzky-Golay filters
Conference
1546-1874
Citations 
PageRank 
References 
0
0.34
0
Authors
2
Name
Order
Citations
PageRank
Sreeram V. Menon100.34
Chandra Sekhar Seelamantula2487.95