Entropy-based subspace separation for multiple frequency estimation

Authors:
Erhan A. Ince;Runyi Yu;Aykut Hocanin
Affiliations:
Department of Electrical and Electronic Engineering, Eastern Mediterranean University, Gazimağusa, via Mersin 10, Turkey;Department of Electrical and Electronic Engineering, Eastern Mediterranean University, Gazimağusa, via Mersin 10, Turkey;Department of Electrical and Electronic Engineering, Eastern Mediterranean University, Gazimağusa, via Mersin 10, Turkey
Venue:
Digital Signal Processing
Year:
2013

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Abstract

A subspace-based algorithm for estimating the order and the frequencies of multiple sinusoids embedded in noise is proposed. The new estimator (referred to as E-MUSIC) uses the entropy of a random variable related to the angles between the signal and noise subspaces as its objective function. Maximizing the entropy tends to achieve uniform angle distribution and thus leads to maximal subspace separation. The entropy-based objective function and the performance of the E-MUSIC algorithm is compared with some reference algorithms in the literature. Simulations which are performed in additive white and colored Gaussian noise show that the E-MUSIC offers an improvement for both model order and multiple frequency estimation. The improvement is more pronounced for high model orders and large SNR values.