Maximum Likelihood Whitening Pre-filtered Total Least Squares for Resolving Closely Spaced Signals

摘要

This paper presents whitening pre-filtered total least squares based on the maximum likelihood technique for root selection to resolve closely spaced signals for linear prediction. A frequency-weighting filter applied to the total least-squares method is commonly used to handle the problem of frequency estimation. This solution provides better performance than the traditional total least-squares technique does when the signal-to-noise ratio is low. However, the performance of total least squares using frequency- weighting filters yields biased effects when the signal-to-noise ratio is high, even worse than the traditional total least-squares method. In view of this, a whitening pre-filtered total least squares based on the maximum likelihood technique for roots selection is introduced. This technique can use the information from the output of the pre-filtered data to eliminate the bias inherent in the frequency-weighting filter method, and most importantly to maintain decent performance levels for a wide range of signal-to-noise ratios.