On spatial smoothing and linear prediction

Author

Krim, H. ; Cozzens, J. ; Proakis, J.

Author_Institution

Dept. of Electr. & Comput. Eng., Northeastern Univ., Boston, MA, USA

fYear

1990

fDate

3-6 Apr 1990

Firstpage

2639

Abstract

The relationship between Cadzow´s signal subspace algorithm and the spatially smoothed minimum-norm algorithm of Tufts-Kumaresan is investigated. It is shown that Cadzow´s algorithm can be realized by subarray averaging lower rank approximations to the array covariance matrix. A data-domain algorithm that is applicable in a correlated signal environment is formulated. This algorithm offers the advantage of lower word length requirements, and obviates the necessity of computing higher order statistics. It may also be extended in a straightforward way to incorporate signal enumeration. Simulation results are given that contrast the performance of this algorithm to the signal eigenvector approach

Keywords

correlation theory; filtering and prediction theory; signal processing; Cadzow´s signal subspace algorithm; Tufts-Kumaresan; array covariance matrix; correlated signal environment; data-domain algorithm; higher order statistics; linear prediction; minimum-norm algorithm; signal eigenvector approach; signal enumeration; spatial smoothing; subarray averaging; word length requirements; Computational modeling; Covariance matrix; Decorrelation; Digital signal processing; Direction of arrival estimation; Higher order statistics; Sensor arrays; Signal processing; Signal processing algorithms; Signal resolution; Smoothing methods; Transmission line matrix methods;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics, Speech, and Signal Processing, 1990. ICASSP-90., 1990 International Conference on

Conference_Location

Albuquerque, NM

ISSN

1520-6149

Type

conf

DOI

10.1109/ICASSP.1990.116158

Filename

116158