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Proceedings Paper

Time-frequency filtering for classifying targets in nonstationary clutter
Author(s): Vikram Thiruneermalai Gomatam; Patrick Loughlin
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Paper Abstract

Classifying underwater targets from their sonar backscatter is often complicated by induced or self-noise (i.e. clutter, reverberation) arising from the scattering of the sonar pulse from non-target objects. Because clutter is inherently nonstationary, and because the propagation environment can induce nonstationarities as well, in addition to any nonstationarities / time-varying spectral components of the target echo itself, a joint phase space approach to target classification has been explored. In this paper, we apply a previously developed minimum mean square time-frequency spectral estimation method to design a bank of time-frequency filters from training data to distinguish targets from clutter. The method is implemented in the ambiguity domain in order to reduce computational requirements. In this domain, the optimal filter (more commonly called a “kernel” in the time-frequency literature) multiples the ambiguity function of the received signal, and then the mean squared distance to each target class is computed. Simulations demonstrate that the class-specific optimal kernel better separates each target from the clutter and other targets, compared to a simple mean-squared distance measure with no kernel processing.

Paper Details

Date Published: 13 June 2014
PDF: 7 pages
Proc. SPIE 9090, Automatic Target Recognition XXIV, 90900E (13 June 2014); doi: 10.1117/12.2050526
Show Author Affiliations
Vikram Thiruneermalai Gomatam, Univ. of Pittsburgh (United States)
Patrick Loughlin, Univ. of Pittsburgh (United States)


Published in SPIE Proceedings Vol. 9090:
Automatic Target Recognition XXIV
Firooz A. Sadjadi; Abhijit Mahalanobis, Editor(s)

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