JEPA-MUSIC: Self-Supervised Predictive Subspace Learning for Multiple Signal Classification

Direction-of-arrival (DoA) estimation is fundamental to modern wireless communication and sensing systems. While the classical multiple signal classification (MUSIC) algorithm offers high angular resolution and clear interpretability, its performance degrades under limited snapshots, low signal-to-noise ratios (SNRs), and coherent sources due to empirical covariance breakdown. Conversely, data-driven methods mitigate such degradation but typically demand large DoA-labeled datasets and yield less transparent inference. To bridge this divide, we propose JEPA-MUSIC, a model-based self-supervised learning framework that integrates representation learning with subspace-based MUSIC inference. Specifically, JEPA-MUSIC constructs paired observations from the same signal scene and employs a tailored joint-embedding predictive architecture (JEPA) to learn to recover a structured, MUSIC-compatible covariance without requiring ground-truth DoA labels. During inference, this recovered covariance directly feeds into standard MUSIC for spatial-spectrum search, preserving its rigorous model-based interpretability. Extensive evaluations across diverse sensing conditions demonstrate that JEPA-MUSIC substantially outperforms classical MUSIC in challenging regimes and matches or surpasses strong data-driven baselines, while achieving higher subspace fidelity, lower tail failure rates, and superior sample efficiency.