An Experimental Study on Fine-Grained Bistatic Sensing of UAV Trajectory via Cellular Downlink Signals

In this letter, a dual-bistatic unmanned aerial vehicles (UAVs) tracking system utilizing downlink Long-Term Evolution (LTE) signals is proposed and demonstrated. Particularly, two LTE base stations (BSs) are exploited as illumination sources. Two passive sensing receivers are deployed at different locations to detect the bistatic Doppler frequencies of the target UAV at different directions according to downlink signals transmitted from their corresponding BSs, such that the velocities of the UAV versus time can be estimated. Hence, the trajectories of the target UAV can be reconstructed. Although both the target UAV and the sensing receivers are around 200 meters away from the illuminating BSs, it is demonstrated by experiments that the tracking errors are below 50 centimeters for 90% of the complicated trajectories, when the distances between the UAV and sensing receivers are less than 30 meters. Note this accuracy is significantly better than the ranging resolution of LTE signals, high-accuracy trajectory tracking for UAV might be feasible via multi-angle bistatic Doppler measurements if the receivers are deployed with a sufficient density.

💡 Research Summary

This paper presents a novel passive sensing framework for high‑precision tracking of low‑altitude unmanned aerial vehicles (UAVs) by exploiting downlink signals from commercial Long‑Term Evolution (LTE) base stations (eNBs). The system consists of two LTE eNBs acting as illumination sources and two spatially separated passive receivers. Each receiver is equipped with two radio‑frequency (RF) chains: a narrow‑beam chain aligned with the line‑of‑sight (LoS) to its associated eNB (reference channel) and a wide‑beam chain that captures the scattered signal from the UAV (surveillance channel). By cross‑correlating the reference and surveillance channels, the bistatic Doppler frequency induced by the UAV motion is extracted for each receiver.

The signal model assumes that the reference channel contains a strong LoS component plus negligible interference, while the surveillance channel comprises the UAV‑scattered echo, static clutter reflections, and noise. After sampling, an interference‑cancellation technique based on multi‑coherent integration (Multi‑CIT) suppresses the static components. The cross‑ambiguity function (CAF) is computed over a sliding window of Nw samples (duration Td) to obtain a Doppler spectrum. An adaptive threshold βi,k(fD) = γ·P·max|R_i,k(fD + pΔf)| is applied to each CAF peak to reject false alarms while preserving true Doppler peaks. When multiple peaks survive, a weighted average with weight α is used; when no peak is present, linear interpolation from neighboring valid detections fills the gap.

The two Doppler measurements (f1,k, f2,k) from the two receivers are related to the UAV’s instantaneous velocity vector vk via a 2×2 geometric matrix Dk that depends on the known positions of the eNBs, receivers, and the current UAV estimate, as well as the wavelengths λ1 and λ2 of the two LTE carriers. The velocity is recovered by vk = Dk⁻¹·