Neyman Pearson Detection of K-Distributed Random Variables

摘要

In this paper a new detection method for sonar imagery is developed in Κ-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 Κ-distributed model. Results illustrating the effectiveness of the Κdistributed detector are presented in terms of probability of detection, false alarm, and correct classification rates for various bottom clutter scenarios.