IEEE 802.11ad-Aided 5-D Sensing with a UAV Swarm in Urban Environment

Aerial base stations mounted on unmanned aerial vehicles (UAVs) support next-generation wireless networks in challenging environments such as urban areas, disaster zones, and remote locations. Further, UAV swarms overcome the challenges of limited battery life and other operational constraints of a single UAV. However, tracking mobile users on the ground by each UAV and the corresponding synchronization between the UAVs is a significant issue that must be addressed before this framework can be deployed in reality. Incorporating additional sensing capabilities to facilitate this additional requirement would introduce significant overhead in terms of hardware, cost, and power to each UAV. Instead, we propose an integrated sensing and communications-enabled swarm UAV system, based on the millimeter-wave IEEE 802.11ad protocol. Further, we show that our proposed system is capable of five-dimensional (5-D) ground target sensing (range, Doppler velocity, azimuth, elevation, and polarization) in an urban environment. Numerical experiments using realistic models demonstrate and validate the performance of 5-D sensing using our proposed 802-11ad-aided UAV system.

💡 Research Summary

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The paper proposes a novel integrated sensing and communications (ISAC) architecture that equips a swarm of unmanned aerial vehicles (UAVs) with the IEEE 802.11ad millimeter‑wave (mmWave) protocol to perform five‑dimensional (5‑D) ground‑target sensing. The authors argue that adding dedicated radar hardware to each UAV would increase weight, cost, and power consumption, which is undesirable for battery‑limited aerial platforms. By re‑using the 512‑sample Golay complementary sequences embedded in the IEEE 802.11ad preamble as radar waveforms, the system achieves high‑resolution range estimation without any modification to the existing communication standard.

The UAV swarm consists of eight drones arranged in a uniform circular array (UCA). Each drone acts as a single antenna element; together they provide narrow, digitally‑steered beams. During transmission, both horizontal (H) and vertical (V) polarizations are emitted, enabling the extraction of polarization information (σ_HH, σ_VV, σ_HV, σ_VH) from the scattered returns. The propagation model explicitly includes a direct line‑of‑sight (LOS) path and a ground‑reflected path for each target, capturing realistic urban multipath and clutter effects. The received signal at each UAV is a superposition of the four polarization‑dependent radar cross‑section (RCS) components, ground clutter, and additive white Gaussian noise.

Signal processing proceeds on a three‑dimensional data cube of size N (antennas) × P (fast‑time samples) × Q (slow‑time packets). The steps are: (1) matched filtering across fast‑time using the complex conjugate of the Golay sequence, (2) a 1‑D FFT across slow‑time to obtain Doppler bins, (3) an IFFT to reconstruct a 2‑D range‑Doppler ambiguity function for each antenna element, (4) iterative CLEAN processing to remove the strongest target’s point‑spread response and reveal weaker targets, and (5) a 2‑D MUSIC algorithm applied to the residual data across the antenna array to estimate azimuth (ϕ) and elevation (θ) for each detected target. The Doppler frequency is converted to velocity via v = f λ/2.

Simulation parameters reflect a realistic 60 GHz mmWave system: 1.76 GHz bandwidth, 2 µs pulse repetition interval, 4 ms coherent processing interval, maximum unambiguous range of 44 m, range resolution of 8.5 cm, maximum unambiguous velocity of 625 m/s, and velocity resolution of 0.3 m/s. Three isotropic point scatterers are placed at 5 m, 10 m, and 15 m from the swarm with varying azimuth/elevation angles and velocities of 4 m/s, 18 m/s, and 10 m/s respectively. Their RCS values differ between H and V polarizations to test the system’s polarization discrimination. Ground clutter is modeled with a –5 dB coefficient.

Results demonstrate that the strongest target is clearly visible in the raw range‑Doppler map, along with its weaker ground‑reflected echo. After the first CLEAN iteration, the second target emerges; a second iteration reveals the weakest third target. The subsequent 2‑D MUSIC step accurately localizes all three targets in azimuth and elevation, with peak amplitudes reflecting the relative RCS strengths. Identical behavior is observed for the V‑polarization channel, confirming that the dual‑polarization approach successfully extracts polarization signatures. The system thus achieves simultaneous estimation of range, velocity, azimuth, elevation, and polarization—constituting true 5‑D sensing—using only the standard IEEE 802.11ad waveform and a modest UAV swarm.

Key contributions include: (i) leveraging an existing commercial Wi‑Gig standard for radar without extra hardware, (ii) demonstrating that a modest‑size UAV swarm can provide the spatial diversity needed for 3‑D angle estimation and polarization discrimination, (iii) incorporating realistic LOS, ground‑bounce, and clutter models to validate performance in urban settings, and (iv) presenting a complete processing chain (matched filtering, CLEAN, MUSIC) that can be implemented on lightweight onboard processors. The work points toward cost‑effective, power‑efficient aerial platforms capable of both high‑throughput communication and high‑resolution environmental sensing, opening avenues for smart‑city monitoring, disaster response, and autonomous traffic management.