Explosive hazards in current and former conflict zones are a threat to both military and civilian personnel. As a result,
much effort has been dedicated to identifying automated algorithms and systems to detect these threats. However, robust
detection is complicated due to factors like the varied composition and anatomy of such hazards. In order to solve this
challenge, a number of platforms (vehicle-based, handheld, etc.) and sensors (infrared, ground penetrating radar, acoustics,
etc.) are being explored. In this article, we investigate the detection of side attack explosive ballistics via a vehicle-mounted
acoustic sensor. In particular, we explore three acoustic features, one in the time domain and two on synthetic aperture
acoustic (SAA) beamformed imagery. The idea is to exploit the varying acoustic frequency profile of a target due to its
unique geometry and material composition with respect to different viewing angles. The first two features build their
angle specific frequency information using a highly constrained subset of the signal data and the last feature builds its
frequency profile using all available signal data for a given region of interest (centered on the candidate target location).
Performance is assessed in the context of receiver operating characteristic (ROC) curves on cross-validation experiments
for data collected at a U.S. Army test site on different days with multiple target types and clutter. Our preliminary results
are encouraging and indicate that the top performing feature is the unrolled two dimensional discrete Fourier transform
(DFT) of SAA beamformed imagery.