Minimum-norm method without eigendecomposition

Author

Shaw, Amab K. ; Xia, Wei

Author_Institution

Dept. of Electr. Eng., Wright State Univ., Dayton, OH, USA

Volume

1

Issue

1

fYear

1994

Firstpage

12

Lastpage

14

Abstract

The minimum-norm method (MNM) for high-resolution angles-of-arrival (AOA) estimation relies on special-purpose hardware or software for obtaining the signal and noise subspace eigenvectors of autocorrelation (AC) matrices. It is shown that the discrete Fourier transform (DFT) of the AC matrix (DFT-of-AC) essentially performs an equivalent task of separating the signal and noise subspaces. Furthermore, when the signal-subspace part of the DFT-of-AC vectors are used in MNM, almost identical high-resolution AOA estimates are produced.<>

Keywords

array signal processing; correlation methods; eigenvalues and eigenfunctions; fast Fourier transforms; minimisation; AC matrices; AOA estimation; DFT-of-AC; autocorrelation matrices; discrete Fourier transform; eigendecomposition; high-resolution angles-of-arrival estimation; minimum-norm method; noise subspace eigenvectors; signal subspace eigenvectors; Autocorrelation; Covariance matrix; Frequency estimation; Hardware; Iterative algorithms; Iterative methods; Narrowband; Optimization methods; Signal processing algorithms; Signal resolution;

fLanguage

English

Journal_Title

Signal Processing Letters, IEEE

Publisher

ieee

ISSN

1070-9908

Type

jour

DOI

10.1109/97.295314

Filename

295314