Classification of Clutter Types in Active Sonar Using Spatial Image Processing Techniques

Clutter (e.g., reverberation and bottom structures) can lead to excessive false alarm rates (identifying clutter as submarines) in antisubmarine warfare (ASW) active sonar systems. False alarm reduction is an important marine challenge in an era where ASW has shifted from noisy submarines in deep waters to quiet submarines in highly cluttered littoral regions. In this paper, physically motivated image processing features have been developed and are extracted from digitized clutter images for the purpose of classifying these images. The ability to automatically classify these images can ultimately be applied in situ to tactical systems to improve automated tracking and classification. The clutter images are generated by averaging mid-frequency active sonar acoustic returns, which have been convolved with an appropriate measurement error kernel, from sea trial archives over several pings of history. Match filtered, normalized mid-frequency data from the full two-dimensional tactical field are considered, which provides contextual information and avoids analyzing the complicated time series data directly. Marginal probability distributions of features extracted from each class and multi-class classification results using log-likelihood techniques are presented.