Performance of detection and classification of targets in active sonar systems may be degraded in the presence of stationary clutter, ownship motion-induced clutter, and active interference. Applied Research in Acoustics’ (ARiA) sparse estimation algorithms estimate and separate targets, reverberation, and mutual interference signals from a cluttered signal and enable novel classification features to be computed from sparse representations. Integration of ARiA’s advanced signal and information processing enables automated and semi-automated sonar signal detection and classification, thus reducing operator workload. ARiA’s signal and information processing enhancements are targeted for the AN/SQQ89A(V)15 Integrated Undersea Warfare (USW) Combat System Suite’s pulsed active sonar (PAS) function segment (PASFS) echo tracker classifier (ETC). However, the developed algorithms are suitable for integration into most active sonar or radar platforms.