DocumentCode :
19570
Title :
Subspace Alignment and Separation for Multiple Frequency Estimation
Author :
Runyi Yu ; Ince, E.A. ; Hocanin, A.
Author_Institution :
Dept. of Electr. & Electron. Eng., Eastern Mediterranean Univ., Gazimagusa, Turkey
Volume :
22
Issue :
1
fYear :
2015
fDate :
Jan. 2015
Firstpage :
16
Lastpage :
20
Abstract :
In this letter, a new subspace based estimator that can effectively provide the order and frequencies of multiple sinusoids in noise is proposed. The estimator, referred to as SAS-Est (Subspace Aligning and Separating Estimator), simultaneously seeks to separate the steering vectors from the noise subspace and align them to the signal subspace. The angles between subspaces and the generalized Kullback-Leibler divergence are used in characterizing the alignment and separation. Minimizing the divergence leads to maximal subspace separation and best alignment, thus allowing improved performance. Simulations in additive white Gaussian noise show that the new estimator offers an improvement for both model order and frequency estimation. When compared with other methods, the improvement is more pronounced for high model orders and low signal-to-noise ratio values.
Keywords :
AWGN; frequency estimation; signal processing; additive white Gaussian noise; generalized Kullback-Leibler divergence; maximal subspace separation; model order; multiple frequency estimation; subspace aligning and separating estimator; Covariance matrices; Estimation; Frequency estimation; Multiple signal classification; Signal to noise ratio; Vectors; Generalized Kullback-Leibler divergence; multiple signal classification (MUSIC); order estimation; parameter estimation; subspace alignment; subspace separation;
fLanguage :
English
Journal_Title :
Signal Processing Letters, IEEE
Publisher :
ieee
ISSN :
1070-9908
Type :
jour
DOI :
10.1109/LSP.2014.2346252
Filename :
6874503
Link To Document :
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