arXiv subjects

Azhar Hasan

Publications and source records attributed to Azhar Hasan.

3 recordsLinked to original sources

arXiv2026

Design and Experimental Validation of a Multiband Cross-Polarization Conversion (CPC) Metasurface for Radar Cross Section (RCS) Reduction

Radar cross-section (RCS) reduction is a fundamental requirement in modern stealth technology, playing a critical role in the low-observable performance of aerial and naval platforms. Among the various passive RCS reduction strategies, including radar-absorbing materials, absorptive coatings, and artificially engineered surfaces, metasurface-based cross-polarization conversion has emerged as a compelling approach owing to its structural simplicity and low profile. In this work, a single-layer cross-polarization conversion (CPC) metasurface developed on a cost-effective FR4 dielectric substrate (relative permittivity 4.4, loss tangent 0.02) is proposed for multiband RCS reduction. The designed structure achieves a polarization conversion ratio (PCR) exceeding 95% at three distinct operating frequencies of 7.8 GHz, 11.7 GHz, and 18 GHz, spanning the C-, X-, and Ku-bands, which directly translates into a monostatic RCS reduction exceeding 10 dBsm at the corresponding bands. The metasurface further demonstrates stable polarization conversion performance under oblique incidence up to 60 degrees, confirming its suitability for wide-angle illumination conditions encountered in practical deployment scenarios. Experimental validation conducted in an anechoic chamber confirms close agreement with full-wave electromagnetic simulations, substantiating the reliability of the fabricated prototype. The proposed design offers a lightweight, low-cost, and high-performance candidate for multiband stealth and low-observable platform applications.

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arXiv2026

Feature-Level Robustness of Physics-Guided Micro-Doppler Descriptors for classification of Drones and Birds

Micro-Doppler signatures are a proven modality for discriminating between drones and birds, but their reliability degrades in low-SNR, data-constrained settings where deep learning models often fail. This paper presents a systematic study of ten statistical and physics-motivated handcrafted features for micro-Doppler classification under controlled signal degradation, using a publicly available 77 GHz FMCW radar dataset. Spectrograms are corrupted with additive white Gaussian noise, phase noise, and their combination across SNRs from -10 dB to 10 dB and phase noise levels from 1 to 10 degrees. Features are evaluated using stratified 5-fold cross-validation with Support Vector Machine and Random Forest classifiers, using fixed hyperparameters across all noise conditions. On clean data, both models achieve mean accuracy of 0.916, with F1 scores of 0.909 (SVM) and 0.892 (Random Forest). Under severe noise, entropy-based and side-lobe features remain robust, yielding F1 scores up to 0.773 and 0.831, respectively. Permutation-based importance analysis shows that some features retain complementary discriminative power even when their individual importance is low. These results highlight the value of principled feature design and provide insight into feature robustness for interpretable radar classification systems.

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arXiv2025

Incorporating Ephemeral Traffic Waves in A Data-Driven Framework for Microsimulation in CARLA

This paper introduces a data-driven traffic microsimulation framework in CARLA that reconstructs real-world wave dynamics using high-fidelity time-space data from the I-24 MOTION testbed. Calibration of road networks in microsimulators to reproduce ephemeral phenomena such as traffic waves for large-scale simulation is a process that is fraught with challenges. This work reconsiders the existence of the traffic state data as boundary conditions on an ego vehicle moving through previously recorded traffic data, rather than reproducing those traffic phenomena in a calibrated microsim. Our approach is to autogenerate a 1 mile highway segment corresponding to I-24, and use the I-24 data to power a cosimulation module that injects traffic information into the simulation. The CARLA and cosimulation simulations are centered around an ego vehicle sampled from the empirical data, with autogeneration of "visible" traffic within the longitudinal range of the ego vehicle. Boundary control beyond these visible ranges is achieved using ghost cells behind (upstream) and ahead (downstream) of the ego vehicle. Unlike prior simulation work that focuses on local car-following behavior or abstract geometries, our framework targets full time-space diagram fidelity as the validation objective. Leveraging CARLA's rich sensor suite and configurable vehicle dynamics, we simulate wave formation and dissipation in both low-congestion and high-congestion scenarios for qualitative analysis. The resulting emergent behavior closely mirrors that of real traffic, providing a novel cosimulation framework for evaluating traffic control strategies, perception-driven autonomy, and future deployment of wave mitigation solutions. Our work bridges microscopic modeling with physical experimental data, enabling the first perceptually realistic, boundary-driven simulation of empirical traffic wave phenomena in CARLA.

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