  Advancements in Sonar image capture have opened the door to powerful
classification schemes for automatic target recognition (ATR. Recent work has
particularly seen the application of sparse reconstruction-based classification
(SRC) to sonar ATR, which provides compelling accuracy rates even in the
presence of noise and blur. Existing sparsity based sonar ATR techniques
however assume that the test images exhibit geometric pose that is consistent
with respect to the training set. This work addresses the outstanding open
challenge of handling inconsistently posed test sonar images relative to
training. We develop a new localized block-based dictionary design that can
enable geometric, i.e. pose robustness. Further, a dictionary learning method
is incorporated to increase performance and efficiency. The proposed SRC with
Localized Pose Management (LPM), is shown to outperform the state of the art
SIFT feature and SVM approach, due to its power to discern background clutter
in Sonar images.
