Sonar imaging has seen vast improvements over the last few decades due in part to advances in synthetic aperture Sonar (SAS). Because of this, sophisticated classification techniques originally developed for other tasks can 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 even in settings with little training. We present a coherent strategy for using SRC for Sonar ATR that retains SRC's robustness while also being able to handle targets with diverse geometric arrangements. Our method, pose corrected sparsity (PCS), incorporates state-of-the-art dictionary learning schemes on localized block extractions which we show produces compelling classification results on the RAWSAS dataset.