DocumentCode :
681685
Title :
3M relationship pattern for detection and estimation of unknown frequencies for unknown number of sinusoids based on eigenspace analysis of Hankel matrix
Author :
Ahmed, Arif ; Hu, Y.F.
Author_Institution :
Sch. of Eng., Design & Technol., Univ. of Bradford, Bradford, UK
fYear :
2013
fDate :
2-3 Dec. 2013
Firstpage :
1
Lastpage :
6
Abstract :
We develop a novel approach to estimate the n unknown constituent frequencies of a sinusoidal signal that comprises of unknown number, n, of sinusoids of unknown phases and unknown amplitudes. The approach has been applied to multiple sinusoidal signals in the presence of white Gaussian noise with varying signal to noise ratio (SNR). The approach is based on eigenspace analysis of Hankel matrix formed with the samples from averaged frequency spectrum of the signal obtained through multiple measurements. The eigenspace analysis is based on the newly developed 3M relationship which reflects and exploits the relationship between the consecutive sets of Maximum, Middle and Minimum eigenvalues of square symmetric matrix of the Hankel matrix. The 3M relationship exhibits a pattern in line with the order of the Hankel matrix and leads to parametric estimation of the constituent sinusoids. This paper also presents the relationship equation between the size of 3M relationship pattern and the dimensions of the Hankel matrix. The performance of the developed approach has been tested to correctly estimate multiple constituent frequencies within a noisy signal.
Keywords :
Gaussian noise; Hankel matrices; frequency estimation; signal processing; 3M relationship pattern; Hankel matrix; SNR; constituent sinusoids; eigenspace analysis; eigenvalues; frequency spectrum; multiple constituent frequency; multiple measurements; multiple sinusoidal signals; noisy signal; parametric estimation; signal to noise ratio; square symmetric matrix; unknown constituent frequency; unknown frequency detection; unknown frequency estimation; white Gaussian noise; Hankel matrix; Harmonic analysis; sinusoidal parameter estimation; spectral analysis;
fLanguage :
English
Publisher :
iet
Conference_Titel :
Intelligent Signal Processing Conference 2013 (ISP 2013), IET
Conference_Location :
London
Electronic_ISBN :
978-1-84919-774-8
Type :
conf
DOI :
10.1049/cp.2013.2058
Filename :
6740507
Link To Document :
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