UAV Detection Based on OTFS ISAC System

The detection and sensing of Unmanned aerial vehicles (UAVs) has become increasingly critical due to concerns about airspace security and urban surveillance. This paper proposes a micro-Doppler (MD) based Unmanned aerial vehicle (UAV) detection method within an orthogonal time-frequency space (OTFS) integrated sensing and communication (ISAC) framework. The approach employs Kullback-Leibler (KL) divergence, likelihood ratio testing, and the Neyman-Pearson (NP) criterion to distinguish UAV echoes from static clutter. A core innovation of this framework is the use of range filtering in the delay-time (DT) domain combined with MD feature extraction, which enables the differentiation between static clutter and dynamic UAV signals within the OTFS-ISAC framework. A software-defined radio (SDR) prototype is implemented to validate the feasibility of the method in real-world scenarios. The experimental results demonstrate a detection probability of up to 99.99 % at a false alarm rate of 10<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−2</sup> confirming the viability of OTFS-based ISAC for UAV detection under practical constraints.