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
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;
Journal_Title :
Signal Processing Letters, IEEE
DOI :
10.1109/LSP.2014.2346252