arXiv subjects

Aline Eid

Publications and source records attributed to Aline Eid.

6 recordsLinked to original sources

arXiv2026

A Metalens-based Bicycle Safety Reflector for Autonomous Vehicle Radars

With the rising number of interactions between autonomous or sensor-assisted vehicles -- especially in poor weather conditions -- come the need and opportunity for a new class of bicycle safety reflectors designed to enhance cyclist visibility to radars. To this effect, the first retrodirective planar metalens-based tag operating in the millimeter-wave automotive frequency range is proposed. The compact, lightweight ($0.61~\mathrm{g}$) design consists of two layers: a metalens layer and a patch antenna pixel layer. The metalens focuses incoming plane waves from different incidence angles onto corresponding patch antenna pixels on the second layer, which re-radiate the signal back through the metalens, enabling retrodirective operation. The proposed tag was thoroughly evaluated, demonstrating reliable detection beyond 70 m and a peak monostatic radar cross section (RCS) of $3.54~\mathrm{dBsm}$ with stable retrodirectivity over $\pm 40^\circ$, providing an average gain improvement of $7.58~\mathrm{dB}$ and an RCS enhancement of $15.16~\mathrm{dB}$ relative to a lens-less reference. A realistic deployment scenario on a metallic bicycle demonstrated up to a 110x improvement in its detectability at broadside. These results highlight the potential of the proposed passive tag to operate as a low-cost, lightweight, and easily integrable bicycle safety reflector for next-generation autonomous vehicle radar systems.

eess.SP↗

arXiv2026

An ISAC-ready Full-Duplex Backscatter Architecture for the mmWave IoT

Achieving long-range, high data rate, concurrent two-way mmWave communication with power-constrained IoT devices is fundamental to scaling future ubiquitous sensing systems, yet the substantial power demands and high cost of mmWave hardware have long stood in the way of practical deployment. This paper presents Armstrong, the first mmWave full-duplex backscatter tag architecture, charting a genuinely low-cost path toward high-performance mmWave connectivity for ISAC systems. Armstrong operates in full duplex at ranges beyond 88m and beyond 200m in downlink alone, delivering 20x the reach of state-of-the-art systems while being over 100x cheaper than existing mmWave backscatter platforms. Enabling this leap is a novel low-power regenerative amplifier that provides 30 dB of gain while consuming only 7.7 mW during active transmission, paired with a regenerative rectifier that achieves state-of-the-art sensitivity down to -60 dBm. We integrate our circuits on a compact PCB and evaluate it across diverse downlink and uplink scenarios, where it achieves 1 Kbps BERs of less than 10^{-1} at 200m and 88m, respectively, demonstrating resilient, high-quality communication even at extended ranges.

cs.NI↗

arXiv2025

DragonFly: Single mmWave Radar 3D Localization of Highly Dynamic Tags in GPS-Denied Environments

The accurate localization and tracking of dynamic targets, such as equipment, people, vehicles, drones, robots, and the assets that they interact with in GPS-denied indoor environments is critical to enabling safe and efficient operations in the next generation of spatially aware industrial facilities. This paper presents DragonFly , a 3D localization system of highly dynamic backscatter tags using a single MIMO mmWave radar. The system delivers the first demonstration of a mmWave backscatter system capable of exploiting the capabilities of MIMO radars for the 3D localization of mmID tags moving at high speeds and accelerations at long ranges by introducing a critical Doppler disambiguation algorithm and a fully integrated cross-polarized dielectric lens-based mmID tag consuming a mere 68 uW. DragonFly was extensively evaluated in static and dynamic configurations, including on a flying quadcopter, and benchmarked against multiple baselines, demonstrating its ability to track the positions of multiple tags with a median 3D accuracy of 12 cm at speeds and acceleration on the order of 10 m/s and 4 m/s^2 and at ranges of up to 50m.

cs.RO↗

arXiv2025

RadarSplat: Radar Gaussian Splatting for High-Fidelity Data Synthesis and 3D Reconstruction of Autonomous Driving Scenes

High-Fidelity 3D scene reconstruction plays a crucial role in autonomous driving by enabling novel data generation from existing datasets. This allows simulating safety-critical scenarios and augmenting training datasets without incurring further data collection costs. While recent advances in radiance fields have demonstrated promising results in 3D reconstruction and sensor data synthesis using cameras and LiDAR, their potential for radar remains largely unexplored. Radar is crucial for autonomous driving due to its robustness in adverse weather conditions like rain, fog, and snow, where optical sensors often struggle. Although the state-of-the-art radar-based neural representation shows promise for 3D driving scene reconstruction, it performs poorly in scenarios with significant radar noise, including receiver saturation and multipath reflection. Moreover, it is limited to synthesizing preprocessed, noise-excluded radar images, failing to address realistic radar data synthesis. To address these limitations, this paper proposes RadarSplat, which integrates Gaussian Splatting with novel radar noise modeling to enable realistic radar data synthesis and enhanced 3D reconstruction. Compared to the state-of-the-art, RadarSplat achieves superior radar image synthesis (+3.4 PSNR / 2.6x SSIM) and improved geometric reconstruction (-40% RMSE / 1.5x Accuracy), demonstrating its effectiveness in generating high-fidelity radar data and scene reconstruction. A project page is available at https://umautobots.github.io/radarsplat.

cs.CV↗

arXiv2023

3D Self-Localization of Drones using a Single Millimeter-Wave Anchor

We present the design, implementation, and evaluation of MiFly, a self-localization system for autonomous drones that works across indoor and outdoor environments, including low-visibility, dark, and GPS-denied settings. MiFly performs 6DoF self-localization by leveraging a single millimeter-wave (mmWave) anchor in its vicinity - even if that anchor is visually occluded. MmWave signals are used in radar and 5G systems and can operate in the dark and through occlusions. MiFly introduces a new mmWave anchor design and mounts light-weight high-resolution mmWave radars on a drone. By jointly designing the localization algorithms and the novel low-power mmWave anchor hardware (including its polarization and modulation), the drone is capable of high-speed 3D localization. Furthermore, by intelligently fusing the location estimates from its mmWave radars and its IMUs, it can accurately and robustly track its 6DoF trajectory. We implemented and evaluated MiFly on a DJI drone. We demonstrate a median localization error of 7cm and a 90th percentile less than 15cm, even when the anchor is fully occluded (visually) from the drone.

cs.RO↗

arXiv2023

A Handheld Fine-Grained RFID Localization System with Complex-Controlled Polarization

There is much interest in fine-grained RFID localization systems. Existing systems for accurate localization typically require infrastructure, either in the form of extensive reference tags or many antennas (e.g., antenna arrays) to localize RFID tags within their radio range. Yet, there remains a need for fine-grained RFID localization solutions that are in a compact, portable, mobile form, that can be held by users as they walk around areas to map them, such as in retail stores, warehouses, or manufacturing plants. We present the design, implementation, and evaluation of POLAR, a portable handheld system for fine-grained RFID localization. Our design introduces two key innovations that enable robust, accurate, and real-time localization of RFID tags. The first is complex-controlled polarization (CCP), a mechanism for localizing RFIDs at all orientations through software-controlled polarization of two linearly polarized antennas. The second is joint tag discovery and localization (JTDL), a method for simultaneously localizing and reading tags with zero-overhead regardless of tag orientation. Building on these two techniques, we develop an end-to-end handheld system that addresses a number of practical challenges in self-interference, efficient inventorying, and self-localization. Our evaluation demonstrates that POLAR achieves a median accuracy of a few centimeters in each of the x/y/z dimensions in practical indoor environments.

cs.NI↗