Title :
Sinusoidal frequency estimation by approximate MUSIC method
Author :
Karhunen, Juha ; Joutsensalo, Jyrki
Author_Institution :
Lab. of Comput. & Inf. Sci., Helsinki Univ. of Technol., Espoo, Finland
Abstract :
Two efficient methods avoiding eigenvector computation are proposed for approximating the signal subspace in terms of the Fourier transform. The resulting approximations are used to substitute for the signal eigenvectors in MUSIC. The proposed methods perform almost the same as MUSIC at high SNRs and provide often clearly better results at low SNRs. They seem to be more robust than MUSIC against overestimation of the number of sinusoids.<>
Keywords :
correlation theory; fast Fourier transforms; matrix algebra; parameter estimation; spectral analysis; ACM-MUSIC; DFT-MUSIC; Fourier transform; approximate MUSIC method; autocorrelation matrix; high SNR; low SNR; robustness; signal eigenvectors; signal subspace; sinusoidal frequency estimation; Additive white noise; Array signal processing; Eigenvalues and eigenfunctions; Fourier transforms; Frequency estimation; Laboratories; Multiple signal classification; Robustness; Sensor arrays; White noise;
Conference_Titel :
Spectrum Estimation and Modeling, 1990., Fifth ASSP Workshop on
Conference_Location :
Rochester, NY, USA
DOI :
10.1109/SPECT.1990.205603
