Maximum likelihood and common factor analysis-based blind adaptive spatial filtering for cyclostationary signals

Authors:
S. V. Schell;W. A. Gardner
Affiliations:
Dept. of Electr. & Comput. Eng., Pennsylvania State Univ., University Park, PA, USA;Dept. of Electr. Eng., Toronto Univ., Ont., Canada
Venue:
ICASSP '93 Proceedings of the Acoustics, Speech, and Signal Processing, 1993. ICASSP-93 Vol 4., 1993 IEEE International Conference on - Volume 04
Year:
1993

Citing 2
Cited 1

Modern multivariate statistical analysis, a graduate course and handbook

Modern multivariate statistical analysis, a graduate course and handbook
Blind adaptive spatiotemporal filtering for wide-bandcyclostationary signals

IEEE Transactions on Signal Processing

A reduced-rank spectral approach to multi-sensor enhancement of large numbers of sinusoids in unknown noise fields

Signal Processing

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Abstract

A blind adaptive spatio-temporal filter for unknown cyclostationary signals in noise is derived in two ways by maximizing a constrained conditional likelihood function and by solving a common factor analysis problem. It is shown that specific choices of free constraint-parameters within the resulting beamformer structure yield the existing cross spectral self-coherence restoral (SCORE) algorithms and conjugate cross-SCORE algorithms which blindly adapt and antenna array to extract signals having specified cyclostationarity properties from interference and noise. It is also show that other choices can substantially improve the convergence properties of SCORE algorithms.