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Proceedings Paper

Time-sequenced adaptive filtering using a modified P-vector algorithm
Author(s): Robert L. Williams
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Paper Abstract

An adaptive algorithm and two stage filter structure were developed for adaptive filtering of certain classes of signals that exhibit cyclostationary characteristics. The new modified P-vector algorithm (mPa) eliminates the need for a separate desired signal which is typically required by conventional adaptive algorithms. It is then implemented in a time-sequenced manner to counteract the nonstationary characteristics typically found in certain radar and bioelectromagnetic signals. Initial algorithm testing is performed on evoked responses generated by the visual cortex of the human brain with the objective, ultimately, to transition the results to radar signals. Each sample of the evoked response is modeled as the sum of three uncorrelated signal components, a time-varying mean (M), a noise component (N), and a random jitter component (Q). A two stage single channel time-sequenced adaptive filter structure was developed which improves convergence characteristics by de coupling the time-varying mean component from the `Q' and noise components in the first stage. The EEG statistics must be known a priori and are adaptively estimated from the pre stimulus data. The performance of the two stage mPa time-sequenced adaptive filter approaches the performance for the ideal case of an adaptive filter having a noiseless desired response.

Paper Details

Date Published: 22 October 1996
PDF: 10 pages
Proc. SPIE 2846, Advanced Signal Processing Algorithms, Architectures, and Implementations VI, (22 October 1996); doi: 10.1117/12.255428
Show Author Affiliations
Robert L. Williams, Air Force Wright Lab. (United States)

Published in SPIE Proceedings Vol. 2846:
Advanced Signal Processing Algorithms, Architectures, and Implementations VI
Franklin T. Luk, Editor(s)

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