Improved frequency estimation using total least square approach

The resolution of the closely spaced sinusoids becomes poor as the SNR of the received signal reduces. 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 to reduce the noise effect from both the observation vector and the LP data matrix. At low SNR, the performance of this frequency estimator is found to be better than the frequency estimator using the Principal Eigenvector (PE) method which is proposed by Tufts and Kumaresan. They used the SVD of the LP data matrix to reduce the noise effect from the data matrix only.