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

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Abstract

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.

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Authors and Affiliations

  1. Department of Nursing and Health Sciences, Tung Wah College, Kowloon, Hong Kong

    C. F. So

  2. Department of Electronic Engineering, City University of Hong Kong, Kowloon, Hong Kong

    S. H. Leung

Authors
  1. C. F. So
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  2. S. H. Leung
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Correspondence to C. F. So.

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So, C.F., Leung, S.H. Maximum Likelihood Whitening Pre-filtered Total Least Squares for Resolving Closely Spaced Signals. Circuits Syst Signal Process 34, 2739–2747 (2015). https://doi.org/10.1007/s00034-015-9983-x

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  • DOI: https://doi.org/10.1007/s00034-015-9983-x

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