From Signals to Evidence: Interpretable Underwater Acoustic Target Sensing with Large Language Models - Part I: Framework and Recognition
Teaching AI to explain what it hears underwater, not just guess
Researchers combined underwater sound analysis with large language models to build a system that doesn't just identify ships—it explains what physical evidence led to that identification. The system can accurately recognize vessels and estimate specific acoustic signatures like shaft frequency and blade-passing frequency, grounded in actual physics rather than black-box pattern matching.
Navy and coast guard systems currently flag acoustic targets without explaining why, making it hard for operators to trust alerts or learn from mistakes. This framework could turn sonar into a tool that shows its reasoning—identifying a specific ship class while pointing to the exact acoustic features that clinched the call. That transparency matters for training personnel, catching false alarms, and building confidence in automated ocean surveillance.