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Proceedings Paper

Neyman Pearson detection of K-distributed random variables
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Paper Abstract

In this paper a new detection method for sonar imagery is developed in K-distributed background clutter. The equation for the log-likelihood is derived and compared to the corresponding counterparts derived for the Gaussian and Rayleigh assumptions. Test results of the proposed method on a data set of synthetic underwater sonar images is also presented. This database contains images with targets of different shapes inserted into backgrounds generated using a correlated K-distributed model. Results illustrating the effectiveness of the K-distributed detector are presented in terms of probability of detection, false alarm, and correct classification rates for various bottom clutter scenarios.

Paper Details

Date Published: 29 April 2010
PDF: 12 pages
Proc. SPIE 7664, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XV, 76640Q (29 April 2010); doi: 10.1117/12.851350
Show Author Affiliations
J. Derek Tucker, Naval Surface Warfare Ctr. Panama City Div. (United States)
Colorado State Univ. (United States)
Mahmood R. Azimi-Sadjadi, Colorado State Univ. (United States)

Published in SPIE Proceedings Vol. 7664:
Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XV
Russell S. Harmon; John H. Holloway Jr.; J. Thomas Broach, Editor(s)

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