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Aviel Glam

Publications and source records attributed to Aviel Glam.

6 recordsLinked to original sources

Giant Rotational Meta-Doppler from Genetically Designed Superscatterers

The motion of a rigid body interacting with a wave leaves spectral signatures, with the Doppler shift as the dominant contribution. Since any motion can be decomposed into translational and rotational components, rotational Doppler provides additional information about the object's dynamics. In the electromagnetic domain, rotating objects generally produce rotational Doppler, or micro-Doppler, signals determined by the symmetry and spatial structure of the scattering process. For objects that are subwavelength or comparable in size to the wavelength, the response is typically dominated by the lowest dipolar scattering channel, so the leading spectral component commonly appears at twice the angular frequency. Here, we introduce the concept of artificially engineered rotational micro-Doppler by designing a compact, strongly scattering structure that operates through a high-order multipolar cascade of resonances, thereby producing a giant enhancement. Magneto-electric arrays composed of strongly coupled electric and magnetic resonators are optimized in the GHz range using a covariance matrix adaptation genetic algorithm to maximize the micro-Doppler frequency. Unlike conventional higher-order multipole designs used in superscatterers for a specific angle of incidence and polarization, our approach jointly optimizes excitation and scattering under radar-relevant conditions for a rotating blade. The resulting arrays exhibit a giant rotational meta-micro-Doppler response, exceeding the dipolar limit by two orders of magnitude and mapping rotations of tens of hertz into the kilohertz range. Beyond its fundamental significance, this mapping has practical value because it shifts rotor micro-Doppler signatures well above slow-moving radar clutter, thereby improving the detectability of slow motion.

physics.app-ph

Optically Transparent Meta-Grating Embedded in Rear Windshields for Automotive Radar Detection

Radar plays a crucial role in automotive safety by enabling reliable object detection, thereby assisting drivers and, prospectively, serving as one of the primary sensors in autonomous driving. The radar visibility of a road participant depends on its radar cross-section (RCS). While RCS is an inherent property, enhancing it, similar to using reflective vests for optical visibility, can significantly improve radar detection through cooperative target design. However, modern vehicles are not designed for this purpose, and embedded reflectors are not utilized due to the industry's conservative approach and the limited space available on the vehicle's exterior. Rear windshields offer a vast unused area, but they must still serve their primary function and remain transparent. We propose utilizing this area by embedding a reflecting surface that accounts for the interrogation scenario geometry and the angular tilt of the rear windshield, ensuring the wave is retroreflected back to the radar. The surface is realized as an array of thin conductive wires with a periodicity that provides in-phase excitation for the design incidence angle. Given that automotive radars operate in the millimeter-wave regime (77-81 GHz), large-scale surfaces with sub-millimeter manufacturing accuracy are required. This is achieved by imprinting conductive inks, composed of silver nanoparticles and binders, into grooves in the glass. The fabricated 10x10 sq. sm. sample, with around 90% optical transparency, demonstrates an RCS of 8 sq. m., surpassing the typical RCS of a car. Extrapolating this performance to the entire rear window with an embedded meta grating, a typical RCS of 1000 sq. m. can be achieved, thereby enhancing the detectability range by nearly an order of magnitude. Smart windows enable advanced applications in wireless communication, such as automotive scenarios, IoT, and many others.

physics.app-ph

Drone Carry-on Weight and Wind Flow Assessment via Micro-Doppler Analysis

Remote monitoring of drones has become a global objective due to emerging applications in national security and managing aerial delivery traffic. Despite their relatively small size, drones can carry significant payloads, which require monitoring, especially in cases of unauthorized transportation of dangerous goods. A drone's flight dynamics heavily depend on outdoor wind conditions and the carry-on weight, which affect the tilt angle of a drone's body and the rotation velocity of the blades. A surveillance radar can capture both effects, provided a sufficient signal-to-noise ratio for the received echoes and an adjusted postprocessing detection algorithm. Here, we conduct a systematic study to demonstrate that micro-Doppler analysis enables the disentanglement of the impacts of wind and weight on a hovering drone. The physics behind the effect is related to the flight controller, as the way the drone counteracts weight and wind differs. When the payload is balanced, it imposes an additional load symmetrically on all four rotors, causing them to rotate faster, thereby generating a blade-related micro-Doppler shift at a higher frequency. However, the impact of the wind is different. The wind attempts to displace the drone, and to counteract this, the drone tilts to the side. As a result, the forward and rear rotors rotate at different velocities to maintain the tilt angle of the drone body relative to the airflow direction. This causes the splitting in the micro-Doppler spectra. By performing a set of experiments in a controlled environment, specifically, an anechoic chamber for electromagnetic isolation and a wind tunnel for imposing deterministic wind conditions, we demonstrate that both wind and payload details can be extracted using a simple deterministic algorithm based on branching in the micro-Doppler spectra.

physics.app-ph

Blank Space: Adaptive Causal Coding for Streaming Communications Over Multi-Hop Networks

In this work, we introduce Blank Space Adaptive Causal Random Linear Network Coding (BS-AC-RLNC), a novel coding scheme designed to mitigate the triplet trade-off between throughput-delay-efficiency in multi-hop networks. BS-AC-RLNC leverages the physical limitations of the network, considering the bottleneck from each node to the destination. In particular, this approach introduces a light-computational re-encoding algorithm, called AC-RLNC (NET), implemented independently at intermediate nodes. NET adaptively adjusts the Forward Error Correction (FEC) rates and schedules idle periods. It incorporates two distinct suspension mechanisms: 1) Blank Space Period, accounting for the forward-channels bottleneck, and 2) No-New No-FEC approach, based on data availability. We present theoretical lower and upper bounds on in-order delivery delay, goodput, and throughput; in the case of in-order delay, we further derive a mean bound. These analytical results are extended to the multicast scenario, providing a broader understanding of the algorithm's performance under diverse network conditions. The experimental results achieve significant improvements in resource efficiency, demonstrating a 20% reduction in channel usage compared to baseline RLNC solutions. Notably, these efficiency gains are achieved while maintaining competitive throughput and delay performance, ensuring improved resource utilization does not compromise network performance.

cs.IT

3D Genetic Metamaterials for Scattering Maximization

The rapidly growing volume of drone air traffic demands improved radar surveillance systems and increased detection reliability in challenging conditions. The scattering cross-section, which characterizes a target's radar visibility, is a key element in detection schemes and thus becomes a primary objective in civilian applications. Here, we introduce a concept of genetically designed metamaterials, specifically engineered to enhance scattering for end-fire incidence scenarios. Multi-layer stacks of arrays, encompassing strongly coupled electric and magnetic resonators, demonstrated above 1 m^2 broadband scattering at 10 GHz, despite having an end-fire physical cross-section smaller than one squared wavelength. Those performances, crucial for effective civil radar air traffic monitoring, facilitate exploring highly scattering structures as labels for small airborne targets. This objective has been demonstrated with a set of outdoor experiments with the DJI Mini 2 drone. Lightweight, conformal add-ons with significantly high scattering cross-sections can serve as auxiliary tools to empower passive monitoring systems, thereby providing an additional layer of security in urban airspace.

physics.app-ph

Micro-Doppler-Coded Drone Identification

The forthcoming era of massive drone delivery deployment in urban environments raises a need to develop reliable control and monitoring systems. While active solutions, i.e., wireless sharing of a real-time location between air traffic participants and control units, are of use, developing additional security layers is appealing. Among various surveillance systems, radars offer distinct advantages by operating effectively in harsh weather conditions and providing high-resolution reliable detection over extended ranges. However, contrary to traditional airborne targets, small drones and copters pose a significant problem for radar systems due to their relatively small radar cross-sections. Here, we propose an efficient approach to label drones by attaching passive resonant scatterers to their rotor blades. While blades themselves generate micro-Doppler rotor-specific signatures, those are typically hard to capture at large distances owing to small signal-to-noise ratios in radar echoes. Furthermore, drones from the same vendor are indistinguishable by their micro-Doppler signatures. Here we demonstrate that equipping the blades with multiple resonant scatterers not only extends the drone detection range but also assigns it a unique micro-Doppler encoded identifier. By extrapolating the results of our laboratory and outdoor experiments to real high-grade radar surveillance systems, we estimate that the clear-sky identification range for a small drone is approximately 3-5 kilometers, whereas it would be barely detectable at 1000 meters if not labeled. This performance places the proposed passive system on par with its active counterparts, offering the clear benefits of reliability and resistance to jamming.

physics.app-ph