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Proceedings Paper

Data-driven optimization of time and frequency resolution for radar transmitter identification
Author(s): Bradford W. Gillespie; Les E. Atlas
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Paper Abstract

An entirely new set of criteria for the design of kernels for time-frequency representations (TFRs) has been recently proposed. The goal of these criteria is to produce kernels which will enable accurate classification without explicitly defining, a priori,the underlying features that differentiate individual classes. These kernels, which are optimized to discriminate among multiple classes of signal, are referred to as signal class-dependent kernels, or simply class- dependent kernels. Here this technique is applied to the problem of radar transmitter identification. Several modifications to our earlier approach have been incorporated into the processing, and are detailed here. It will be shown that an overall classification rate of 100 percent can be achieved using our new augmented approach, provided exact time registration of the data is available. In practice, time registration can not be guaranteed. Therefore,the robustness of our technique to data misalignment is also investigated. A measurable performance loss is incurred in this case. A method for mitigating this loss by incorporating our class-dependent methodology within the framework of classification trees is proposed.

Paper Details

Date Published: 2 October 1998
PDF: 8 pages
Proc. SPIE 3461, Advanced Signal Processing Algorithms, Architectures, and Implementations VIII, (2 October 1998); doi: 10.1117/12.325704
Show Author Affiliations
Bradford W. Gillespie, Univ. of Washington (United States)
Les E. Atlas, Univ. of Washington (United States)

Published in SPIE Proceedings Vol. 3461:
Advanced Signal Processing Algorithms, Architectures, and Implementations VIII
Franklin T. Luk, Editor(s)

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