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Abstract
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Periodic signals are encountered in many applications. Such signals can be modelled by a weighted sum of sinusoidal components whose frequencies are integer multiples of a fundamental frequency. Given a data set, the fundamental frequency can be estimated in many ways including a maximum likelihood (ML) approach. Unfortunately, the ML estimator has a very high computational complexity, and the more inaccurate, but faster correlationbased estimators are therefore often used instead. In this paper, we propose a fast algorithm for the evaluation of the ML cost function for complexvalued data over all frequencies on a Fourier grid and up to a maximum model order. The proposed algorithm significantly reduces the computational complexity to a level not far from the complexity of the popular harmonic summation method which is an approximate ML estimator.
Original language  English 

Title of host publication  23rd European Signal Processing Conference (EUSIPCO), 2015 
Publisher  IEEE Press 
Publication date  1 Sep 2015 
Pages  589  593 
ISBN (Electronic)  9780992862633 
DOIs  
Publication status  Published  1 Sep 2015 
Event  2015 23rd European Signal Processing Conference (EUSIPCO)  Nice, France Duration: 31 Aug 2015 → 4 Sep 2015 
Conference
Conference  2015 23rd European Signal Processing Conference (EUSIPCO) 

Country  France 
City  Nice 
Period  31/08/2015 → 04/09/2015 
Series  Proceedings of the European Signal Processing Conference 

ISSN  20761465 
Projects
 1 Finished

RTC: Computational Oriented Realtime Convex Optimization in Signal Processing
Jensen, T., Jensen, S. H., Larsen, T., Giacobello, D., Dahl, J. & Diehl, M.
The Danish Council for Independent Research Technology and Production Sciences, Independent Research Fund Denmark  Sapere Aude
01/06/2014 → 30/06/2017
Project: Research