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Bayesian information criterion for multidimensional sinusoidal order selection

 
: Xiong, J.; Liu, K.; Costa, J.P.C.L. da; Wang, W.-Q.

:

Institute of Electrical and Electronics Engineers -IEEE-; IEEE Signal Processing Society:
IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017. Proceedings : March 5-9, 2017, Hilton New Orleans Riverside, New Orleans, Louisiana, USA
Piscataway, NJ: IEEE, 2017
ISBN: 978-1-5090-4117-6
ISBN: 978-1-5090-4116-9
ISBN: 978-1-5090-4118-3
pp.3106-3110
International Conference on Acoustics, Speech, and Signal Processing (ICASSP) <42, 2017, New Orleans/La.>
English
Conference Paper
Fraunhofer IIS ()

Abstract
Detecting the sinusoidal order is a prerequisite step for parametric multidimensional sinusoidal frequency estimation methods, whose applications range from radar and wireless communications to nuclear magnetic resonance spectroscopy. Although the Bayesian information criterion (BIC) has been commonly applied for model order selection, its application to sinusoidal order estimation is recent. By means of estimation of Fisher information matrix, we extend the 1-D BIC to multidimensional case for multidimensional sinusoidal order selection. The multidimensional BIC is shown in simulations to outperform the state-of-the-art algorithms in terms of probability of correct detection.

: http://publica.fraunhofer.de/documents/N-502637.html