Scheduled System Maintenance:
On Monday, April 27th, IEEE Xplore will undergo scheduled maintenance from 1:00 PM - 3:00 PM ET (17:00 - 19:00 UTC). No interruption in service is anticipated.
By Topic

Total least squares approach for frequency estimation using linear prediction

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Rahman, M.D. ; Virginia Polytechnic Institute and State University, Blacksburg, VA ; Kai-Bor Yu

The resolution of the estimated closely spaced frequencies of the multiple sinusoids degrades as the signal-to-noise ratio (SNR) of the received signal becomes low. This resolution can be improved by using the total least squares (TLS) method in solving the linear prediction (LP) equation. This approach makes use of the singular value decomposition (SVD) of the augmented matrix for low rank approximation to reduce the noise effect from both the observation vector and the LP data matrix simultaneously. Comparison is made to the principle eigenvector (PE) method of Tufts and Kumaresan, both on theoretical and experimental grounds. The TLS algorithm exhibits superior performance over the PE method where low rank approximation is applied to the data matrix only.

Published in:

Acoustics, Speech and Signal Processing, IEEE Transactions on  (Volume:35 ,  Issue: 10 )