A Practical Approach to FMCW Radar Deconvolution in the Sea Ice Domain
This paper presents a practical step-by-step approach to Frequency Modulated Continuous Wave (FMCW) radar nonlinearity correction (deconvolution), utilizing surface-based Ku- and Ka-band radar data collected over nilas ice within a newly-opened sea ice lead during the 2019/2020 MOSAiC expedition. Tw...
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2024-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/10758266/ |
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| author | Thomas Newman Julienne C. Stroeve Vishnu Nandan Rosemary C. Willatt James B. Mead Robbie Mallett Michel Tsamados Marcus Huntemann Stefan Hendricks Gunnar Spreen Rasmus T. Tonboe |
| author_facet | Thomas Newman Julienne C. Stroeve Vishnu Nandan Rosemary C. Willatt James B. Mead Robbie Mallett Michel Tsamados Marcus Huntemann Stefan Hendricks Gunnar Spreen Rasmus T. Tonboe |
| author_sort | Thomas Newman |
| collection | DOAJ |
| description | This paper presents a practical step-by-step approach to Frequency Modulated Continuous Wave (FMCW) radar nonlinearity correction (deconvolution), utilizing surface-based Ku- and Ka-band radar data collected over nilas ice within a newly-opened sea ice lead during the 2019/2020 MOSAiC expedition. Two performance metrics are introduced to evaluate deconvolution effectiveness: the spurious free dynamic range (SFDR), which quantifies sidelobe suppression, and the leading edge width (LEW), which quantifies the improvement in surface return clarity. The impact of deconvolution waveforms on different survey dates, radar polarizations, and surface types is examined using echograms and quantitative metrics. Deconvolution results in a maximum SFDR increase of 28 dB, with a maximum 3 dB decline in deconvolution performance observed over an 8-day period and a maximum decline of 15 dB observed over a 71-day period. The LEW values indicate that the effectiveness of deconvolution in enhancing interface clarity depends on the combination of pre-deconvolution sidelobe shape, prominence of the surface return, the influence of snowpack returns, as well as a time-dependent reduction in deconvolution performance. Deconvolution significantly improves surface return clarity for cross-polarized radar data, where weak surface returns are obscured by returns from within the snowpack. The results demonstrate that deconvolution performance is most effective shortly after deconvolution waveform characterization. Therefore, it is recommended to perform at least weekly calibrations using a large metal sheet and ideally calibration before/after data collection to ensure optimal deconvolution performance and effective sidelobe suppression. |
| format | Article |
| id | doaj-art-c4f725202deb40b1b7cfb7698f3ccd95 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
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| series | IEEE Access |
| spelling | doaj-art-c4f725202deb40b1b7cfb7698f3ccd952024-11-29T00:01:28ZengIEEEIEEE Access2169-35362024-01-011217490117493310.1109/ACCESS.2024.350250210758266A Practical Approach to FMCW Radar Deconvolution in the Sea Ice DomainThomas Newman0https://orcid.org/0009-0005-0440-8366Julienne C. Stroeve1https://orcid.org/0000-0001-7316-8320Vishnu Nandan2https://orcid.org/0000-0001-9643-6658Rosemary C. Willatt3https://orcid.org/0000-0003-2512-562XJames B. Mead4Robbie Mallett5https://orcid.org/0000-0002-1069-6529Michel Tsamados6https://orcid.org/0000-0001-7034-5360Marcus Huntemann7Stefan Hendricks8https://orcid.org/0000-0002-1412-3146Gunnar Spreen9https://orcid.org/0000-0003-0165-8448Rasmus T. Tonboe10https://orcid.org/0000-0003-1463-4832Centre for Polar Observation and Modelling, Earth Sciences, University College London, London, U.K.Centre for Earth Observation Science, University of Manitoba, Winnipeg, MB, CanadaDepartment of Electronics and Communication Engineering, Amrita Vishwa Vidyapeetham, Bengaluru Campus, Bengaluru, Karnataka, IndiaCentre for Polar Observation and Modelling, Earth Sciences, University College London, London, U.K.ProSensing, Amherst, MA, USADepartment of Physics and Technology, UiT The Arctic University of Norway, Tromsø, NorwayCentre for Polar Observation and Modelling, Earth Sciences, University College London, London, U.K.Institute of Environmental Physics, University of Bremen, Bremen, GermanyAlfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Bremerhaven, GermanyInstitute of Environmental Physics, University of Bremen, Bremen, GermanyDepartment of Space Research and Technology, Technical University of Denmark, Lyngby, DenmarkThis paper presents a practical step-by-step approach to Frequency Modulated Continuous Wave (FMCW) radar nonlinearity correction (deconvolution), utilizing surface-based Ku- and Ka-band radar data collected over nilas ice within a newly-opened sea ice lead during the 2019/2020 MOSAiC expedition. Two performance metrics are introduced to evaluate deconvolution effectiveness: the spurious free dynamic range (SFDR), which quantifies sidelobe suppression, and the leading edge width (LEW), which quantifies the improvement in surface return clarity. The impact of deconvolution waveforms on different survey dates, radar polarizations, and surface types is examined using echograms and quantitative metrics. Deconvolution results in a maximum SFDR increase of 28 dB, with a maximum 3 dB decline in deconvolution performance observed over an 8-day period and a maximum decline of 15 dB observed over a 71-day period. The LEW values indicate that the effectiveness of deconvolution in enhancing interface clarity depends on the combination of pre-deconvolution sidelobe shape, prominence of the surface return, the influence of snowpack returns, as well as a time-dependent reduction in deconvolution performance. Deconvolution significantly improves surface return clarity for cross-polarized radar data, where weak surface returns are obscured by returns from within the snowpack. The results demonstrate that deconvolution performance is most effective shortly after deconvolution waveform characterization. Therefore, it is recommended to perform at least weekly calibrations using a large metal sheet and ideally calibration before/after data collection to ensure optimal deconvolution performance and effective sidelobe suppression.https://ieeexplore.ieee.org/document/10758266/Arcticdeconvolutionfrequency modulated continuous wave (FMCW)MOSAiC expeditionnonlinearity correctionpolarimetry |
| spellingShingle | Thomas Newman Julienne C. Stroeve Vishnu Nandan Rosemary C. Willatt James B. Mead Robbie Mallett Michel Tsamados Marcus Huntemann Stefan Hendricks Gunnar Spreen Rasmus T. Tonboe A Practical Approach to FMCW Radar Deconvolution in the Sea Ice Domain IEEE Access Arctic deconvolution frequency modulated continuous wave (FMCW) MOSAiC expedition nonlinearity correction polarimetry |
| title | A Practical Approach to FMCW Radar Deconvolution in the Sea Ice Domain |
| title_full | A Practical Approach to FMCW Radar Deconvolution in the Sea Ice Domain |
| title_fullStr | A Practical Approach to FMCW Radar Deconvolution in the Sea Ice Domain |
| title_full_unstemmed | A Practical Approach to FMCW Radar Deconvolution in the Sea Ice Domain |
| title_short | A Practical Approach to FMCW Radar Deconvolution in the Sea Ice Domain |
| title_sort | practical approach to fmcw radar deconvolution in the sea ice domain |
| topic | Arctic deconvolution frequency modulated continuous wave (FMCW) MOSAiC expedition nonlinearity correction polarimetry |
| url | https://ieeexplore.ieee.org/document/10758266/ |
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