Autonomous feature discovery, representation and analysis for small SONAR targets using domain cognizant machine learning techniques

This thesis uses machine learning to help develop an understanding of how the physics and geometry of different underwater objects affect how SONAR interacts with them. The key contributions are designing novel machine learning tools that use complex numbers, which incorporate phase information critical to physical interpretation of sonar targets.