Intrusion Detection of UAVs in Cell-Free Networks with Clock Asynchronism

Target sensing in cell-free networks with clock asynchronism presents a formidable challenge in the field of Integrated Sensing and Communication (ISAC). Existing methods exhibit various limitations especially in multipath propagation scenarios with nondominant static path power. Traditional calibration schemes are typically sensitive to multi-path power due to the presence of multiple targets. In this paper, we propose an asynchronous calibration scheme, which amalgamates time-domain phase tracking with the traditional cross-antenna cross-correlation (CACC) approach. Therefore, the robustness of eliminating random phase noise in multi-target scenarios can be enhanced, and the adverse effects of dynamic target intermodulation on target sensing are mitigated through channel reconstruction. Furthermore, the Mirrored-MUSIC algorithm is employed as the sensing strategy to yield estimations of Doppler and delay, effectively addressing the mirrored ambiguity issues induced by CACC. The efficacy of the proposed scheme is sub-stantiated through simulations and experimental results within the unmanned aerial vehicle (UAV) intrusion detection scenarios. The experimental results show that the proposed scheme can effectively mitigate random phase offset and achieve estimations of high precision and resolution.