The widespread use of UAS control links with high‑speed, long‑distance protocols (e.g., ExpressLRS with LoRa/CSS and FLRC modes, and OFDM‑based proprietary systems such as DJI OcuSync) highlights broader challenges in spectrum assurance and situational awareness. Indiscriminate, wideband interference is often energy‑inefficient and can significantly degrade other services – especially in crowded license‑exempt ISM bands (2.4 GHz, 868/915 MHz) and, in some regions, adjacent licensed services. Rather than relying on coarse techniques such as wide-spectrum blocking, this work explores Specific Emitter Identification (SEI) as a way to attribute transmissions to particular devices or device classes.

The research is not limited to a single protocol or modulation, but the framework is intended to generalize to other modulations used in UAS and IoT systems (e.g., FLRC/GFSK, OFDM). Prior SEI approaches that rely heavily on extremely fine spectral features can be sensitive to carrier drift, channel variation, and Doppler, especially in dynamic airborne environments. Typical UAS Doppler shifts are modest but can still perturb narrowband features and interact with channel estimation. To improve robustness, we emphasize device-dependent RF imperfections while explicitly accounting for receiver calibration, SNR variation, temperature drifts, and transmit power changes so that the learned fingerprints reflect the transmitter rather than the measurement setup.

Building on these fingerprints, we develop a real-time SEI and interference-management framework on an edge computing platform paired with a Software-Defined Radio (SDR) that supports precise, hardware-timed operations (e.g., FPGA or on-radio scheduling). Many modern links employ frequency hopping or chirp signaling with sequences that are deterministic for the participants but appear pseudo-random to external observers. Although recovering hop state in general is nontrivial, a large class of low-latency telemetry and control links exhibits quasi-periodic traffic patterns and protocol-structured timing. We exploit this temporal structure with a “look-ahead” synchronization mechanism that predicts likely packet opportunities and aligns analysis or intervention actions at symbol or field granularity, subject to the constraints imposed by coding, interleaving, and protocol resilience.