28 July 1986 Eigenvector Methods in Signal Processing
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Proceedings Volume 0614, Highly Parallel Signal Processing and Architectures; (1986); doi: 10.1117/12.960495
Event: O-E/LASE'86 Symposium, 1986, Los Angeles, CA, United States
Abstract
Eigendecomposition and Singular Value Decomposition of matrices have become important comput at ional tools in signal processing systems. This may be a "natural evolution" since, for some time now, linear algebra (i.e., the algebra of vector spaces) has been providing processing tools for problems such as direction-finding (DF) and Spectral Analysis. The concept of the Signal Subspace has emerged as a fruitful means of characterizing useful structure in sensor data and has led to new methods and algorithms in signal processing. Since the Signal Subspace is an invariant subspace which can be computed via the Eigendecomposition of data matrices, such methods are often referred to as "Eigenvector methods of Signal Processing". The purpose of this paper is to present and discuss such methods as applied to signal processing problems such as; - Multiple signal detection - Multiple signal parameter estimation and demodulation - Multiple source location (e.g., direction finding)
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Ralph Schmidt, "Eigenvector Methods in Signal Processing", Proc. SPIE 0614, Highly Parallel Signal Processing and Architectures, (28 July 1986); doi: 10.1117/12.960495; https://doi.org/10.1117/12.960495
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KEYWORDS
Signal processing

Signal detection

Polarization

Antennas

Sensors

Linear algebra

Time series analysis

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