Title :
Maximum likelihood estimation of multiple frequencies with constraints to guarantee unit circle roots
Author_Institution :
Dept. of Electr. Eng., Wright State Univ., Dayton, OH, USA
fDate :
3/1/1995 12:00:00 AM
Abstract :
An approximate maximum-likelihood estimator (MLE) of multiple exponentials converts the frequency estimation problem into a problem of estimating the coefficients of a z-polynomial with roots at the desired frequencies. Theoretically, the roots of the estimated polynomial should fall on the unit circle, but MLE, as originally proposed, does not guarantee unit circle roots. This drawback sometimes causes merged frequency estimates, especially at low SNR. If all the sufficient conditions for the z-polynomial to have unit circle roots are incorporated, the optimization problem becomes too nonlinear and it loses the desirable weighted-quadratic structure of MLE. In the present paper, the exact constraints are imposed on each of the first-order factors corresponding to individual frequencies for ensuring unit circle roots. The constraints are applied during optimization alternately for each frequency. In the absence of any merged frequency estimates, the RMS values more closely approach the theoretical Cramer-Rao (CR) bound at low SNR levels
Keywords :
frequency estimation; maximum likelihood estimation; optimisation; polynomial matrices; signal representation; Cramer-Rao bound; first-order factors; frequency estimation problem; maximum-likelihood estimator; merged frequency estimates; multiple exponentials; multiple frequencies; optimization problem; unit circle roots; z-polynomial; Constraint optimization; Covariance matrix; Frequency conversion; Frequency estimation; Maximum likelihood estimation; Polynomials; Signal processing; Signal resolution; Signal to noise ratio; Sufficient conditions;
Journal_Title :
Signal Processing, IEEE Transactions on