A combinatorial approach to automated LOFARGRAM analysis

Abstract

This thesis examines the combination of three algorithms: Graph Theoretic Tracker (GTT), Hough Transform, and Heuristic Search to enhance the detection of spectral tracks of underwater targets in LOFARGRAMS. Previous studies examined these algorithms separately. Here, GTT is used as a pre-processor of the LOFARGRAM display data to locat optimum paths of signals through noise. The line tonals in the output image from GTT are then manipulated by the Hough Transform into clusters of points in parameter space. A Heuristic Search sorting technique is employed to determine cluster centers. These cluster centers are then reconstructed back into line tonals using the inverse Hough Transform formula. The results of this thesis show improvements by taking the Hough Transform of the original LOFARGRAM masked by the output of GTT. The effect of background noise is offset by the accumulation in the parameter space. Subsequently, the recovery of desired tonals is improved.