Neyman Pearson detection of K-distributed random variables

J. Derek Tucker; Mahmood R. Azimi-Sadjadi

doi:10.1117/12.851350

29 April 2010 Neyman Pearson detection of K-distributed random variables

J. Derek Tucker, Mahmood R. Azimi-Sadjadi

Proceedings Volume 7664, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XV; 76640Q (2010) https://doi.org/10.1117/12.851350
Event: SPIE Defense, Security, and Sensing, 2010, Orlando, Florida, United States

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.

Citation Download Citation

J. Derek Tucker and Mahmood R. Azimi-Sadjadi "Neyman Pearson detection of K-distributed random variables", Proc. SPIE 7664, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XV, 76640Q (29 April 2010); https://doi.org/10.1117/12.851350

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