LiteJam: A Lightweight Deep Learning Architecture for Real-Time GNSS Interference Detection and Characterization in UAVs

Global Navigation Satellite System (GNSS) interference poses a serious threat to Unmanned Aerial Vehicles (UAVs), potentially leading to navigation failures, airspace violations, or even loss of flight control. Although deep learning methods have demonstrated strong performance in interference detection and characterization, most models remain too computationally expensive for onboard deployment due to high computational cost. To address this challenge, we propose LiteJam, a lightweight architecture that utilizes pre-correlation in-phase and quadrature (I/Q) data to construct pseudo-image representations without requiring additional hardware. Specifically, LiteJam adopts a multi-scale convolutional architecture to capture interference patterns, employs a dynamic sparse attention mechanism to adaptively emphasize spatio-spectral cues, and leverages a hierarchical multi-head module for interference detection and characterization. Experimental results show that LiteJam outperforms all baselines. The F1-score of interference classification is 95.74%, outperforming lightweight baselines by 4.37%–24.51%, and generalizes well across diverse scenarios, while maintaining high computational efficiency for real-time UAV applications. Our codes are available at https://github.com/CynthiaCYX/LiteJam.