Title
Singular Value Decomposition and Improved Frequency Estimation Using Linear Prediction
Document Type
Article
Date of Original Version
1-1-1982
Abstract
Linear-prediction-based (LP) methods for fitting multiple-sinusoid signal models to observed data, such as the forward-backward (FBLP) method of Nuttall [5] and Ulrych and Clayton [6], are very ill-conditioned. The locations of estimated spectral peaks can be greatly affected by a small amount of noise because of the appearance of outliers. LP estimation of frequencies can be greatly improved at low SNR by singular value decomposition (SVD) of the LP data matrix. The improved performance at low SNR is also better than that obtained by using the eigenvector corresponding to the minimum eigenvalue of the correlation matrix, as is done in Pisarenko's method and its variants. © 1982 IEEE
Publication Title, e.g., Journal
IEEE Transactions on Acoustics, Speech, and Signal Processing
Volume
30
Issue
4
Citation/Publisher Attribution
Tufts, Donald W., and Ramdas Kumaresan. "Singular Value Decomposition and Improved Frequency Estimation Using Linear Prediction." IEEE Transactions on Acoustics, Speech, and Signal Processing 30, 4 (1982): 671-675. doi: 10.1109/TASSP.1982.1163927.