Advancing mmWave Altimetry for Unmanned Aerial Systems: A Signal Processing Framework for Optimized Waveform Design
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 continuo...
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MDPI AG
2024-08-01
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| Series: | Drones |
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| Online Access: | https://www.mdpi.com/2504-446X/8/9/440 |
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| author | Maaz Ali Awan Yaser Dalveren Ali Kara Mohammad Derawi |
| author_facet | Maaz Ali Awan Yaser Dalveren Ali Kara Mohammad Derawi |
| author_sort | Maaz Ali Awan |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-2fe06330eb53453dbb2f1db004549882 |
| institution | OA Journals |
| issn | 2504-446X |
| language | English |
| publishDate | 2024-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Drones |
| spelling | doaj-art-2fe06330eb53453dbb2f1db0045498822025-08-20T01:55:22ZengMDPI AGDrones2504-446X2024-08-018944010.3390/drones8090440Advancing mmWave Altimetry for Unmanned Aerial Systems: A Signal Processing Framework for Optimized Waveform DesignMaaz Ali Awan0Yaser Dalveren1Ali Kara2Mohammad Derawi3Graduate School of Natural and Applied Sciences, Department of Electrical and Electronics Engineering, Atilim University, Ankara 06830, TurkeyDepartment of Electrical and Electronics Engineering, Izmir Bakircay University, Izmir 35665, TurkeyDepartment of Electrical and Electronics Engineering, Gazi University, Ankara 06570, TurkeyDepartment of Electronic Systems, Norwegian University of Science and Technology, 2815 Gjovik, NorwayThis 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.https://www.mdpi.com/2504-446X/8/9/440mmWaveTDM-MIMOaltimetryUASFMCWCFAR |
| spellingShingle | Maaz Ali Awan Yaser Dalveren Ali Kara Mohammad Derawi Advancing mmWave Altimetry for Unmanned Aerial Systems: A Signal Processing Framework for Optimized Waveform Design Drones mmWave TDM-MIMO altimetry UAS FMCW CFAR |
| title | Advancing mmWave Altimetry for Unmanned Aerial Systems: A Signal Processing Framework for Optimized Waveform Design |
| title_full | Advancing mmWave Altimetry for Unmanned Aerial Systems: A Signal Processing Framework for Optimized Waveform Design |
| title_fullStr | Advancing mmWave Altimetry for Unmanned Aerial Systems: A Signal Processing Framework for Optimized Waveform Design |
| title_full_unstemmed | Advancing mmWave Altimetry for Unmanned Aerial Systems: A Signal Processing Framework for Optimized Waveform Design |
| title_short | Advancing mmWave Altimetry for Unmanned Aerial Systems: A Signal Processing Framework for Optimized Waveform Design |
| title_sort | advancing mmwave altimetry for unmanned aerial systems a signal processing framework for optimized waveform design |
| topic | mmWave TDM-MIMO altimetry UAS FMCW CFAR |
| url | https://www.mdpi.com/2504-446X/8/9/440 |
| work_keys_str_mv | AT maazaliawan advancingmmwavealtimetryforunmannedaerialsystemsasignalprocessingframeworkforoptimizedwaveformdesign AT yaserdalveren advancingmmwavealtimetryforunmannedaerialsystemsasignalprocessingframeworkforoptimizedwaveformdesign AT alikara advancingmmwavealtimetryforunmannedaerialsystemsasignalprocessingframeworkforoptimizedwaveformdesign AT mohammadderawi advancingmmwavealtimetryforunmannedaerialsystemsasignalprocessingframeworkforoptimizedwaveformdesign |