Non-Gaussian Target Detection in Sonar Imagery Using the Multivariate Laplace Distribution

This paper introduces a new non-Gaussian detection method for complex-valued synthetic aperture sonar (SAS) imagery. The detection method is based on a multivariate extension of the Laplace distribution derived using a scale mixture of Gaussian distributions. A goodness-of-fit test in the form of a likelihood ratio is then conducted on a sonar imagery data set consisting of high-frequency (HF) and broadband (BB) images coregistered over the same region on the seafloor showing the proposed model's applicability in sonar imagery. Detection based on testing the equality of parameters from two populations is then implemented on a database containing actual SAS images of the seafloor with synthetically generated targets inserted into the images and compared to a similar non-Gaussian technique. Detection performance in this paper is given in terms of receiver-operator characteristic (ROC) curve attributes, probability of detection, and average false alarm rate.