Abstract

The growing proliferation of unmanned aerial vehicles (UAVs) has intensified security concerns, particularly from high-speed unauthorized flights in restricted airspaces. Conventional detection methods based on radar, acoustic, vision, and RF sensing, facing inherent limitations in cost, scalability, and environmental robustness, and lack the coordination necessary for wide-area coverage against fast maneuvering threats. To address these limitations, we propose a RIS and O-RAN assisted framework within the Integrated Sensing and Communication (ISAC) paradigm, adapted for the 3.7 GHz Citizens Broadband Radio Service (CBRS) band. The system leverages programmable RIS to improve signal observability and employs O-RAN distributed intelligence for multi-stage UAV detection. A composite OFDM–FMCW waveform provides high-resolution range–Doppler sensing, and a GRU-based predictive controller enables low-latency RIS adaptation. Experimental results, conducted under realistic deployment constraints, demonstrate that a single sensing node achieves reliable detection sufficient to trigger stage transitions. These results confirm the feasibility of integrating RIS and O-RAN for high-speed UAV sensing.

Citation

Xiaochan Xue, Shucheng Yu, Saurabh Parkar, Yao Zheng, “ROISD: RIS and O-RAN Assisted Intelligent Sensing for UAV Detection,” 2025 IEEE Annual Congress on Artificial Intelligence of Things (AIoT), 2025, pp. 140–147. doi: 10.1109/AIoT66900.2025.00031.

Download BibTeX citation · DOI: 10.1109/AIoT66900.2025.00031