Micro-Doppler Sensing for UAV Flight-State Estimation in ISAC Networks

Abstract

Integrated sensing and communication (ISAC) provides a promising foundation for low-altitude unmanned aerial vehicle (UAV) monitoring. However, most existing studies focus primarily on position and bulk velocity, thus providing limited information about UAV rotor dynamics and orientation. To address this limitation, this paper develops an orthogonal frequency-division multiplexing (OFDM)-ISAC framework that exploits UAV micro-Doppler signatures to estimate operational states, including rotor rate and rotor-axis orientation, thereby providing richer UAV state information beyond conventional ISAC localization and velocity sensing. The framework progresses from single-station micro-Doppler sensing to multi-station rotor-axis estimation. At each base station, micro-Doppler signatures are first extracted and used to estimate the rotor rate. The framework is then extended to a distributed ISAC network, where view-dependent micro-Doppler signatures from multiple sensing directions are fused to estimate the common rotor-axis direction. Since the fusion relies on Doppler-bandwidth features rather than complex channel phases, inter-station carrier-phase synchronization is not required. We show that three geometrically diverse views can locally identify both the rotor-axis direction and the unknown rotor-tip-speed scale. In addition, a feature-aware radar equation bridges blade radar cross section (RCS), sensing range, transmit power, and observation duration to micro-Doppler detection and rotor-rate estimation accuracy. The effects of residual motion and range migration on the coherent processing interval (CPI) are also analyzed. Simulations and experimental measurements validate the proposed framework and demonstrate the feasibility of rotor-rate estimation and multi-station rotor-axis sensing.