Drone Swarms Gain ‘eyes’ Using Existing Wireless Signals for 5D Mapping

Researchers are increasingly exploring unmanned aerial vehicle (UAV) swarms to bolster next-generation wireless networks, particularly in complex urban environments. Akanksha Sneh, Shobha Sundar Ram, and Kumar Vijay Mishra, from the Indraprastha Institute of Information Technology Delhi and the United States DEVCOM Army Research Laboratory, present a novel approach to address the critical challenge of mobile user tracking and UAV synchronisation within these swarms. Their work details an integrated sensing and communications system leveraging the millimeter-wave IEEE 802.11ad protocol, offering a low-overhead solution by simultaneously enabling both connectivity and five-dimensional (5-D) ground target sensing, including range, velocity, azimuth, elevation, and polarisation. This research significantly advances the field by demonstrating the feasibility of accurate 5-D sensing without incurring substantial increases in hardware, cost, or power consumption for each UAV.

This integrated sensing and communication-enabled system leverages the millimeter-wave IEEE 802.11ad protocol to simultaneously track mobile users and provide wireless network support, addressing a critical limitation of single UAV deployments.

The work demonstrates the ability to discern a target’s range, Doppler velocity, azimuth, elevation, and polarization, a comprehensive suite of data previously difficult to obtain without dedicated sensing hardware. This breakthrough circumvents the need for additional sensing capabilities on each UAV, reducing hardware costs, power consumption, and overall system complexity.

The proposed system utilizes the inherent properties of the IEEE 802.11ad protocol, specifically the Golay complementary sequences within its channel estimation field, to perform accurate range estimation. By employing digital beamforming, the UAV swarm directs narrow beams towards targets, enhancing precision and minimising interference.

Numerical experiments, conducted using realistic urban models, validate the performance of this 5-D sensing approach. The research details an IEEE 802.11ad-based system where a swarm of UAVs hovers in place, facilitating ground target detection and tracking. The system transmits radar waveforms embedded within standard communication packets, enabling simultaneous sensing and data transmission.

This innovative approach allows for coordinated multi-angle data collection, improving coverage, scalability, and reliability compared to single UAV systems. The study confirms the feasibility of extending ISAC-enabled UAV swarm technology, previously limited to sub-6GHz frequencies, into the millimeter-wave spectrum.

This advancement has significant implications for next-generation wireless networks, intelligent transportation systems, and disaster response scenarios. By providing comprehensive situational awareness, the system supports applications ranging from autonomous vehicle navigation to search and rescue operations.

The ability to accurately map and monitor dynamic environments with a compact, energy-efficient UAV swarm represents a substantial step towards more resilient and adaptable wireless infrastructure. The research establishes a foundation for future work exploring the integration of artificial intelligence and cyber-twin technologies to further enhance the capabilities of ISAC-enabled UAV swarms.

Integrated UAV swarm radar utilising IEEE 802.11ad channel estimation preambles

Millimeter-wave IEEE 802.11ad forms the basis of an integrated sensing and communications-enabled unmanned aerial vehicle (UAV) swarm designed for ground target sensing in urban environments. The research focuses on achieving five-dimensional (5-D) sensing, encompassing range, Doppler velocity, azimuth, elevation, and polarization, without incurring significant hardware or power overhead on each UAV.

This work proposes a swarm configuration hovering in place to facilitate this 5-D sensing capability. The transmitted signal utilizes the IEEE 802.11ad physical layer (PHY) frame structure, embedding a radar waveform within the channel estimation (CE) preamble. Specifically, the 512-Golay sequence within the preamble serves as the radar waveform, with P discrete samples converted to analogue signals using a digital-to-analogue converter and a Dirac-delta function.

These analogue signals, amplified with √Es energy per sample, are then shaped by a transmit-shaping filter and upconverted to a millimeter-wave frequency, fo. A circular antenna array (UCA) comprising N antenna elements distributes the signal, employing a weight vector, wUCA, assigned to each element according to a quasi-omni direction, as initial target localization is not assumed.

To enable polarization sensing, horizontally and vertically polarized electric fields are transmitted using element patterns denoted as ξH and ξV, respectively. The received signal accounts for both direct line-of-sight (LOS) and ground-reflected paths, modelling propagation vectors, utxm,n, incorporating these paths for both horizontal and vertical polarizations.

The received signals, collected across the UCA, are downconverted and digitized, forming a radar data cube of dimension N × P × Q for each polarization. This cube undergoes radar signal processing, beginning with matched filtering in the frequency domain, achieved by multiplying samples with the complex conjugate of the original Golay sequence. This processing facilitates the 5-D sensing of potential targets, extracting range, velocity, and angular information from the reflected signals.

Millimeter-wave UAV swarm localises mobile ground targets via five-dimensional sensing and polarisation enhancement

Researchers demonstrated five-dimensional (5D) ground target sensing using an integrated sensing and communications-enabled unmanned aerial vehicle (UAV) swarm, achieving range, Doppler velocity, azimuth, elevation, and polarization measurements. The system successfully localized multiple mobile targets in an urban environment, with the lowest detected target strength reaching -25dB in horizontal polarization.

Conversely, the same target exhibited a considerably higher signal-to-noise ratio (SNR) of -8dB in vertical polarization, highlighting the benefit of polarization sensing for detecting weaker signals. Simulation results, using targets travelling at 18m/s and 10m/s, validated the efficacy of the proposed millimeter-wave IEEE 802.11ad-aided UAV system.

After the first CLEAN iteration and static clutter filtering, the second highest strength target was localized alongside the ground reflection in the 2D range-Doppler ambiguity map. Subsequent processing, including a second CLEAN iteration, enabled localization of the third, lowest-strength target in terms of range and Doppler.

The 2D azimuth-elevation map revealed all three targets localized with peak strengths corresponding to target intensity. Static ground clutter was modelled with a coefficient of -5dB, and the azimuth search spanned from -180 degrees to 180 degrees, while the elevation search space varied from 90 degrees to 180 degrees. This work establishes a foundation for extending the system to more complex targets, such as pedestrians and vehicles, and for analysing the impact of the UAV swarm on communication link performance in dynamic urban environments.

Millimeter-wave UAV swarms enable five-dimensional ground target characterisation

Researchers have demonstrated five-dimensional sensing of mobile ground targets using a swarm of unmanned aerial vehicles (UAVs) equipped for integrated sensing and communications. This system, based on the millimeter-wave IEEE 802.11ad protocol, accurately determines the range, Doppler velocity, azimuth, elevation, and polarization of targets in a simulated urban environment.

The approach avoids the need for additional hardware on each UAV by leveraging existing communication capabilities for sensing purposes. Simulation results validate the system’s ability to localize multiple mobile targets even with ground clutter present. Polarization sensing proved particularly useful in detecting weaker targets, as vertically polarized signals exhibited a significantly higher signal-to-noise ratio compared to horizontally polarized signals.

This work establishes a foundation for more advanced sensing applications utilising UAV swarms and offers a potential solution for challenging environments where traditional tracking methods may be limited. The authors acknowledge that the current research focuses on relatively simple targets and future work will extend the system to more complex objects like pedestrians and vehicles.

Further investigation is also planned to assess the impact of the UAV swarm on communication performance in dynamic urban environments. Optimisation of swarm coordination strategies to improve both sensing accuracy and communication efficiency represents another area for future research, aiming to enhance the system’s robustness and adaptability for real-world deployment.