Different signal processing techniques for synthetic aperture acoustic (SAA) and highresolution voxel radar (HRVR) sensing modalities for side-attack explosive ballistic (SAEB) detection are proposed in this thesis. The sensing modalities were vehicle mounted and the data used was collected at an army test site. More specifically, the use of a frequency azimuthal (fraz) feature for SAA and the fusion of a matched filter (MF) and size contrast filter (SCF) for HRVR was explored. For SAA, the focus was to find a signature in the target’s response that would vary as the vehicle’s view on the target changed. For the HRVR, the focus was put on finding objects that were both anomalous (SCF) and target-like (MF). The results in both cases are obtained using receiver operating characteristic (ROC) curves and both are very encouraging.

The development of a computer vision algorithm for use with 3D voxel space radar imagery is observed in this thesis. The goal is to detect explosive hazards present in 3D synthetic aperture radar (SAR) image data. The algorithm consists of three primary stages; a precreener to find areas of interest, clustering for labeling distinct areas, and a classifier. The performance between multiple prescreener methods are compared when using a heuristic classifier. Finally, a convolutional neural network (CNN) is used as a classifier stage and a comparison between a deep network, a shallow network, and human experts is conducted.