caa a22 4500 606206191 CHVBK 20210128101010.0 cr unu---uuuuu 210128e20150801xx s 000 0 eng 10.1007/s00034-015-9983-x doi (NATIONALLICENCE)springer-10.1007/s00034-015-9983-x Maximum Likelihood Whitening Pre-filtered Total Least Squares for Resolving Closely Spaced Signals [Elektronische Daten] [C. So, S. Leung] 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. Springer Science+Business Media New York, 2015 Total least squares nationallicence Frequency estimation nationallicence Pre-filtered nationallicence Linear prediction nationallicence Maximum likelihood nationallicence So C. Department of Nursing and Health Sciences, Tung Wah College, Kowloon, Hong Kong aut Leung S. Department of Electronic Engineering, City University of Hong Kong, Kowloon, Hong Kong aut Circuits, Systems, and Signal Processing Springer US; http://www.springer-ny.com 34/8(2015-08-01), 2739-2747 0278-081X 34:8<2739 2015 34 34 https://doi.org/10.1007/s00034-015-9983-x text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00034-015-9983-x text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- So C. Department of Nursing and Health Sciences, Tung Wah College, Kowloon, Hong Kong aut NATIONALLICENCE 700 1- Leung S. Department of Electronic Engineering, City University of Hong Kong, Kowloon, Hong Kong aut NATIONALLICENCE 773 0- Circuits, Systems, and Signal Processing Springer US; http://www.springer-ny.com 34/8(2015-08-01), 2739-2747 0278-081X 34:8<2739 2015 34 34