Paper Info
Title
Robust frequency estimation based on trimmed correlation in impulsive environments
Abstract
The MUSIC method represents a class of super-resolution methods for frequency estimation. However, it has poor performance in impulsive noise environments due to the presence of outliers. A more robust method called trimmed correlation based-MUSIC (TR-MUSIC) method is proposed in this paper. Through a trimming operation, outliers in the samples participating in the correlation calculation are discarded. The amount of trimming is determined by the Mahalanobis distance in which robust estimates of location and scale are utilized. Frequency estimation results from the eigendecomposition of the trimmed correlation matrix. Corroborating simulations are presented to show the robustness and performance improvement of the proposed method.
Year
DOI
Venue
2005
10.1109/ICASSP.2005.1416028
ICASSP '05). IEEE International Conference
Keywords
Field
DocType
correlation methods,eigenvalues and eigenfunctions,frequency estimation,impulse noise,matrix algebra,signal classification,signal resolution,signal sampling,Mahalanobis distance,TR-MUSIC method,eigendecomposition,impulsive noise environments,location estimates,outliers,performance,robust frequency estimation,samples,scale estimates,super-resolution methods,trimmed correlation based-MUSIC,trimmed correlation matrix
Pattern recognition,Least trimmed squares,Computer science,Mahalanobis distance,White noise,Robustness (computer science),Eigendecomposition of a matrix,Artificial intelligence,Trimmed estimator,Covariance matrix,Trimming
Conference
Volume
ISSN
ISBN
4
1520-6149
0-7803-8874-7
Citations 
PageRank 
References 
0
0.34
2
Authors
2
Name
Order
Citations
PageRank
Binwei Weng117613.82
Kenneth E Barner235439.58