Online Background Reverberation Separation and Target Detection in Active Sonar via Grassmannian Subspace Tracking

Reverberation and clutter suppression is critical for active sonar systems, especially when detecting weak underwater targets in shallow water environments. We propose an online dereverberation algorithm that models reverberation as a low-rank background and targets as sparse outliers. The method utilizes the alternating direction method of multipliers (ADMM) for sparse decomposition, alternating it with subspace tracking on the Grassmann manifold to enable frame-wise adaptation with low latency. Compared with traditional batch-based methods and representative online algorithms, the proposed algorithm achieves comparable suppression performance while demonstrating lower latency and higher computational efficiency. Experiments on real-world sonar datasets demonstrate its effectiveness, robustness, and suitability for deployment on resource-constrained embedded platforms.