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    Achieving Millimeter-Level Accuracy for Weight on Wheels (WoW) in Unmanned Aerial Systems (UAS)
    (Institute of Electrical and Electronics Engineers Inc., 2024) Awan, Maaz Ali; Dalveren, Yaser; Kara, Ali
    Weight on Wheels (WoW) systems are pivotal in aircraft landing, ensuring timely control actions and safety measures. Accurate touchdown estimation is critical, demanding fail-safe mechanisms with multiple redundancies. For unmanned aerial systems (UAS), where size, weight, and power (SWaP) metrics are paramount, software redundancies offer a viable solution with minimal SWaP overhead. FrequencyModulated Continuous Wave (FMCW) Radar Altimeters are crucial for UAS landing approaches. Leveraging software-defined architectures and prioritizing WoW safety requirements, radar waveform specifications can be tailored for precise touchdown detection in UAS, aligning with software redundancy principles. While Zoom FFT (ZFFT) traditionally conserves resources by focusing on specific spectrum portions, this study proposes an alternative approach for millimeter-level range accuracy on mmWave FMCW automotive radar. It includes derivation of waveform specifications from operational requirements, methodological insights, theoretical discourse, and experimental hardware results. The article concludes with authors' discussion and future research directions. © 2024 IEEE.
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    Advancing mmWave Altimetry for Unmanned Aerial Systems: A Signal Processing Framework for Optimized Waveform Design
    (MDPI, 2024) Awan, Maaz Ali; Dalveren, Yaser; Kara, Ali; Derawi, Mohammad
    This research advances millimeter-wave (mmWave) altimetry for unmanned aerial systems (UASs) by optimizing performance metrics within the constraints of inexpensive automotive radars. Leveraging the software-defined architecture, this study encompasses the intricacies of frequency modulated continuous waveform (FMCW) design for three distinct stages of UAS flight: cruise, landing approach, and touchdown within a signal processing framework. Angle of arrival (AoA) estimation, traditionally employed in terrain mapping applications, is largely unexplored for UAS radar altimeters (RAs). Time-division multiplexing multiple input-multiple output (TDM-MIMO) is an efficient method for enhancing angular resolution without compromising the size, weight, and power (SWaP) characteristics. Accordingly, this work argues the potential of AoA estimation using TDM-MIMO to augment situational awareness in challenging landing scenarios. To this end, two corner cases comprising landing a small-sized drone on a platform in the middle of a water body are included. Likewise, for the touchdown stage, an improvised rendition of zoom fast Fourier transform (ZFFT) is investigated to achieve millimeter (mm)-level range accuracy. Aptly, it is proposed that a mm-level accurate RA may be exploited as a software redundancy for the critical weight-on-wheels (WoW) system in fixed-wing commercial UASs. Each stage is simulated as a radar scenario using the specifications of automotive radar operating in the 77-81 GHz band to optimize waveform design, setting the stage for field verification. This article addresses challenges arising from radial velocity due to UAS descent rates and terrain variation through theoretical and mathematical approaches for characterization and mandatory compensation. While constant false alarm rate (CFAR) algorithms have been reported for ground detection, a comparison of their variants within the scope UAS altimetry is limited. This study appraises popular CFAR variants to achieve optimized ground detection performance. The authors advocate for dedicated minimum operational performance standards (MOPS) for UAS RAs. Lastly, this body of work identifies potential challenges, proposes solutions, and outlines future research directions.