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Proceedings Paper

Data-driven time-frequency and time-scale detectors
Author(s): Akbar M. Sayeed
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Paper Abstract

In many practical signal detection problems, the detectors have to be designed from training data. Due to limited training data, which is usually the case, it is imperative to exploit some inherent signal structure for reliable detector design. The signals of interest in a variety of applications manifest such structure in the form of nuisance parameters. However, data-driven design of detectors by exploiting nuisance parameters is virtually impossible in general due to two major difficulties: identifying the appropriate nuisance parameters, and estimating the corresponding detector statistics. We address this problem by using recent results that relate joint signal representations (JSRs), such as time-frequency and time-scale representations, to quadratic detectors for a wide variety of nuisance parameters. We propose a general data-driven framework that: (1) identifies the appropriate nuisance parameters from an arbitrarily chosen finite set, and (2) estimates the second-order statistics that characterize the corresponding JSR-based detectors. Simulation results demonstrate that for limited training data, exploiting the structure of nuisance parameters via our framework can deliver substantial gains in performance as compared to empirical detectors which ignore such structure.

Paper Details

Date Published: 24 October 1997
PDF: 12 pages
Proc. SPIE 3162, Advanced Signal Processing: Algorithms, Architectures, and Implementations VII, (24 October 1997);
Show Author Affiliations
Akbar M. Sayeed, Rice Univ. (United States)

Published in SPIE Proceedings Vol. 3162:
Advanced Signal Processing: Algorithms, Architectures, and Implementations VII
Franklin T. Luk, Editor(s)

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