1 research outputs found
Robust Sonar ATR Through Bayesian Pose Corrected Sparse Classification
Sonar imaging has seen vast improvements over the last few decades due in
part to advances in synthetic aperture Sonar (SAS). Sophisticated
classification techniques can now be used in Sonar automatic target recognition
(ATR) to locate mines and other threatening objects. Among the most promising
of these methods is sparse reconstruction-based classification (SRC) which has
shown an impressive resiliency to noise, blur, and occlusion. We present a
coherent strategy for expanding upon SRC for Sonar ATR that retains SRC's
robustness while also being able to handle targets with diverse geometric
arrangements, bothersome Rayleigh noise, and unavoidable background clutter.
Our method, pose corrected sparsity (PCS), incorporates a novel interpretation
of a spike and slab probability distribution towards use as a Bayesian prior
for class-specific discrimination in combination with a dictionary learning
scheme for localized patch extractions. Additionally, PCS offers the potential
for anomaly detection in order to avoid false identifications of tested objects
from outside the training set with no additional training required. Compelling
results are shown using a database provided by the United States Naval Surface
Warfare Center.Comment: 14 Pages, 16 Figures, Accepted TGAR