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.