Reduced polynomial order linear prediction
Date
1996
Authors
Vis, Marvin L., author
Scharf, Louis L., author
Linebarger, Darel A., author
DeGroat, Ronald D., author
Dowling, Eric M., author
IEEE, publisher
Journal Title
Journal ISSN
Volume Title
Abstract
Reduced rank linear predictive frequency and direction-of-arrival (DOA) estimation algorithms use the singular value decomposition (SVD) to produce a noise-cleaned linear prediction vector. These algorithms then root this vector to obtain a subset of roots, whose angles contain the desired frequency or DOA information. The roots closest to the unit circle are deemed to be the "signal roots." The rest of the roots are "extraneous." The extraneous roots are expensive to calculate. Further, a search must be done to discern the signal roots from the extraneous roots. Here, we present a reduced polynomial order linear prediction method that simplifies the rooting computation for applications where high-speed processing is critical.
Description
Rights Access
Subject
direction-of-arrival estimation
frequency estimation
polynomials
singular value decomposition
prediction theory