Underwater target detection using multiple disparate sonar platforms

UNDERWATER TARGET DETECTION USING MULTIPLE DISPARATE SONAR PLATFORMS The detection of underwater objects from sonar imagery presents a difficult problem due to various factors such as variations in the operating and environmental conditions, presence of spatially varying clutter, and variations in target shapes, compositions, and orientation. Additionally, collecting data from multiple platforms can present more challenging questions such as “how should I collaboratively perform detection to achieve optimal performance?”,”how many platforms should be employed?”, “when do we reach a point of diminishing return when adding platforms?”, or more importantly “when does adding an additional platform not help at all?”. To perform multi-platform detection and answer these questions we use the coherent information among all disparate sources of information and perform detection on the premise that the amount of coherent information will be greater in situations where a target is present in a region of interest within an image versus a situation where our observation strictly consists of background clutter. To exploit the coherent information among the different sources, we recast the standard Neyman-Pearson, Gauss-Gauss detector into the Multi-Channel Coherence Analysis (MCA) framework. The MCA framework allows one to optimally decompose the multi-channel data into a new appropriate coordinate system in order to analyze