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Estimation de la période moyenne des vagues par radar cohérent en bande X intégrant la forêt aléatoire et la régression linéaire

  • role： First author第一作者
  • Affiliation：

    Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering, China Three Gorges University, Yichang 443002, China

    College of Computer and Information Technology, China Three Gorges University, Yichang 443002, China

  • Email：liuh@ctgu.edu.cn
  • Introduction：刘含， 研究方向为无线电海洋遥感。E-mail： liuh@ctgu.edu.cn
LIU Han,  
  • Affiliation：

    Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering, China Three Gorges University, Yichang 443002, China

    College of Computer and Information Technology, China Three Gorges University, Yichang 443002, China

WANG Suyue,  
  • Affiliation：

    Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering, China Three Gorges University, Yichang 443002, China

    College of Computer and Information Technology, China Three Gorges University, Yichang 443002, China

ZHANG Qinghe,  
  • Affiliation：

    Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering, China Three Gorges University, Yichang 443002, China

    College of Computer and Information Technology, China Three Gorges University, Yichang 443002, China

SHEN Zhaoyang

résumé

En raison de la rupture des vagues et de leur évolution, des lignes de groupe et des harmoniques d'ordre supérieur apparaissent dans le spectre nombre d'onde-fréquence, accompagnées d'une diminution de l'énergie de la houle principale. Les lignes de groupe sont souvent considérées comme la principale cause de la surestimation de la période des vagues obtenue par radar micro-ondes cohérent. Des études antérieures ont tenté d'éliminer une partie ou la totalité de l'énergie des lignes de groupe afin d'améliorer la précision de l'estimation de la période des vagues. Cependant, en raison de l'incertitude de la distribution de l'énergie dans le spectre nombre d'onde-fréquence, il existe toujours un certain biais lors de l'inversion de la période des vagues à partir du spectre. À cette fin, cet article propose une méthode d'estimation de la période moyenne des vagues basée sur un modèle intégré de forêt aléatoire et de régression linéaire utilisant un radar cohérent en bande X. Cette méthode estime directement la période des vagues à partir de la séquence spatio-temporelle des vitesses des ondes ; d'abord, la séquence spatio-temporelle des vitesses des échos est dérivée du spectre Doppler temporel obtenu par le radar ; ensuite, les caractéristiques sont extraites de la séquence de vitesses spatio-temporelle et un modèle est construit avec les données ECMWF pour prédire la distance minimale entre les pics, afin de localiser les crêtes et les creux ; enfin, la période moyenne des vagues est inversée à l'aide de la relation entre la longueur d'onde et la période de la houle. L'efficacité de la méthode a été validée par simulation. De plus, une analyse a été réalisée sur un ensemble de données de près de 3 jours collecté par un radar cohérent en bande X déployé le long de la côte de la province du Shandong en Chine. L'erreur quadratique moyenne pour la polarisation verticale (VV) et horizontale (HH) était respectivement de 0,15 et 0,22 s. Les résultats montrent que la méthode permet une estimation en temps réel des paramètres des vagues.

mots-clés

Radar cohérent en bande X; Période moyenne des vagues; Séquence spatio-temporelle des vitesses; Polarisation verticale (VV); Polarisation horizontale (HH); Forêt aléatoire; Estimation en temps réel

References

  1. 1.
    Breiman L. 1996. Bagging predictors. Machine Learning, 24(2): 123-140
  2. 2.
    Breiman L. 2001. Random forests. Machine Learning, 45(1): 5-32
  3. 3.
    Carrasco R, Horstmann J and Seemann J. 2017. Significant wave height measured by coherent X-band radar. IEEE Transactions on Geoscience and Remote Sensing, 55(9): 5355-5365
  4. 4.
    Chen X W and Huang W M. 2022a. Spatial–temporal convolutional gated recurrent unit network for significant wave height estimation from shipborne marine radar data. IEEE Transactions on Geoscience and Remote Sensing, 60: 4201711
  5. 5.
    Chen X W, Huang W M, Zhao C and Tian Y W. 2020. Rain detection from X-band marine radar images: a support vector machine-based approach. IEEE Transactions on Geoscience and Remote Sensing, 58(3): 2115-2123
  6. 6.
    Chen Z Z, Chen X, Zhao C, Li J, Huang W M and Gill E W. 2019. Observation and intercomparison of wave motion and wave measurement using shore-based coherent microwave radar and HF radar. IEEE Transactions on Geoscience and Remote Sensing, 57(10): 7594-7605
  7. 7.
    Chen Z Z, Fan L G, Zhao C and Jin Y. 2012. Ocean wave directional spectrum measurement using microwave coherent radar with six antennas. IEICE Electronics Express, 9(19): 1542-1549
  8. 8.
    Chen Z Z, Wang Z H, Chen X, Zhao C, Xie F and He C. 2017. S-band Doppler wave radar system. Remote Sensing, 9(12): 1302
  9. 9.
    Chen Z Z, Zhang C Y, Zhao C, Chen X and Liu H. 2022b. Peak wave direction measurement using shipboard coherent microwave radar. IEEE Geoscience and Remote Sensing Letters, 19: 4500805
  10. 10.
    Chien H, Cheng H Y and Trizna D B. 2013. Determination of phase difference of backscatter signals from coherent-on-receive microwave marine radar for wave measurement//Proceedings of 2013 OCEANS - San Diego. San Diego: IEEE: 1-6
  11. 11.
    Dankert H and Rosenthal W. 2004. Ocean surface determination from X-band radar-image sequences. Journal of Geophysical Research: Oceans, 109(C4): C04016
  12. 12.
    Deng M, Zhao C, Chen Z Z, Ding F and Wang T. 2022. Wave height and wave period measurements using small-aperture HF radar. IEEE Transactions on Geoscience and Remote Sensing, 60: 4209212
  13. 13.
    Fan S R, Hao D X, Feng Y, Xia K W and Yang W B. 2021. A hybrid model for air quality prediction based on data decomposition. Information, 12(5): 210
  14. 14.
    Fang H, Yang J S, Fan G F, Li C, Shi D W and Perrie W. 2022. Ocean surface wind speed retrieve from co-polarized SAR using composite surface Bragg scattering model. National Remote Sensing Bulletin, 26(6): 1274-1287
  15. 15.
    Feng C H, Disis M L, Cheng C and Zhang L J. 2022. Multimetric feature selection for analyzing multicategory outcomes of colorectal cancer: random forest and multinomial logistic regression models. Laboratory Investigation, 102(3): 236-244
  16. 16.
    Greenlees J N. 1994. Basic wave mechanics: for coastal and ocean engineers, by Robert M. Sorensen. Naval Engineers Journal, 106(4): 227-228
  17. 17.
    Guan J, Liu N B, Wang G Q, Ding H, Dong Y L, Huang Y, Tian K X and Zhang M Y. 2023. Sea-detecting radar experiment and target feature data acquisition for dual polarization multistate scattering dataset of marine targets. Journal of Radars, 12(2): 456-469
  18. 18.
    Hackett E E, Fullerton A M, Merrill C F and Fu T C. 2015. Comparison of incoherent and coherent wave field measurements using dual-polarized pulse-Doppler X-band radar. IEEE Transactions on Geoscience and Remote Sensing, 53(11): 5926-5942
  19. 19.
    Hasselmann D E, Dunckel M and Ewing J A. 1980. Directional wave spectra observed during JONSWAP 1973. Journal of Physical Oceanography, 10(8): 1264-1280
  20. 20.
    Hoerl A E and Kennard R W. 2000. Ridge regression: biased estimation for nonorthogonal problems. Technometrics, 42(1): 80-86
  21. 21.
    Hu Z Q, Chen Z Z, Zhao C and Chen X. 2024. Inversion of wave parameters with shore-based coherent S-band radar using quasi-binary variational mode decomposition. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 17: 8570-8580
  22. 22.
    Huang W M, Liu X L and Gill E W. 2017. Ocean wind and wave measurements using X-band marine radar: a comprehensive review. Remote Sensing, 9(12): 1261
  23. 23.
    Huang Y X, Chen Z Z, Zhao C, Wei Y Y and Chen X. 2024. Wave parameter inversion for shipboard coherent S-band radar under shadow modulation. IEEE Transactions on Geoscience and Remote Sensing, 62: 4207815
  24. 24.
    Hwang P A, Perrie W and Zhang B. 2014. Cross-polarization radar backscattering from the ocean surface and its dependence on wind velocity. IEEE Geoscience and Remote Sensing Letters, 11(12): 2188-2192
  25. 25.
    Hwang P A, Sletten M A and Toporkov J V. 2010. A note on Doppler processing of coherent radar backscatter from the water surface: with application to ocean surface wave measurements. Journal of Geophysical Research: Oceans, 115(C3): C03026
  26. 26.
    Lee P H Y, Barter J D, Beach K L, Hindman C L, Lake B M, Rungaldier H, Shelton J C, Williams A B, Yee R and Yuen H C. 1995. X band microwave backscattering from ocean waves. Journal of Geophysical Research: Oceans, 100(C2): 2591-2611
  27. 27.
    Lee P H Y, Barter J D, Beach K L, Lake B M, Rungaldier H and Thompson H R. 1998. Scattering from breaking gravity waves without wind. IEEE Transactions on Antennas and Propagation, 46(1): 14-26
  28. 28.
    Lee P H Y, Barter J D, Caponi E, Caponi M, Hindman C L and Lake B M. 1996. Wind-speed dependence of small-grazing-angle microwave backscatter from sea surfaces. IEEE Transactions on Antennas and Propagation, 44(3): 333-340
  29. 29.
    Liu H, Chen Z Z and Zhao C. 2022a. A new Doppler model incorporated with free and broken-short waves for coherent S-band wave radar at near-grazing angles. IEEE Transactions on Geoscience and Remote Sensing, 60: 5108311
  30. 30.
    Liu H, Chen Z Z, Zhao C and Wu S T. 2022b. Improvement of mean wave period based on dispersion relation filter using shore-based coherent S-band radar. Journal of Atmospheric and Oceanic Technology, 39(8): 1105-1113
  31. 31.
    Liu N B, Ding H, Huang Y, Dong Y L, Wang G Q and Dong K. 2021. Annual progress of the sea-detecting X-band radar and data acquisition program. Journal of Radars, 10(1): 173-182
  32. 32.
    Liu N B, Dong Y L, Wang G Q, Ding H, Huang Y, Guan J, Chen X L and He Y. 2019. Sea-detecting X-band radar and data acquisition program. Journal of Radars, 8(5): 656-667
  33. 33.
    Longuet-Higgins M S. 1957. The statistical analysis of a random, moving surface. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 249(966): 321-387
  34. 34.
    Pierella F, Bredmose H and Dixen M. 2021. Generation of highly nonlinear irregular waves in a wave flume experiment: spurious harmonics and their effect on the Wave Spectrum. Coastal Engineering, 164: 103816
  35. 35.
    Plant W J. 1997. A model for microwave Doppler sea return at high incidence angles: Bragg scattering from bound, tilted waves. Journal of Geophysical Research: Oceans, 102(C9): 21131-21146
  36. 36.
    Plant W J. 2003a. Microwave sea return at moderate to high incidence angles. Waves in Random Media, 13(4): 339-354
  37. 37.
    Plant W J. 2003b. Bound waves and sea-surface slopes//Proceedings of Oceans 2003. Celebrating the Past ... Teaming Toward the Future. San Diego: IEEE: 1825-1828
  38. 38.
    Plant W J, Dahl P H, Giovanangeli J P and Branger H. 2004. Bound and free surface waves in a large wind-wave tank. Journal of Geophysical Research: Oceans, 109(C10): C10002
  39. 39.
    Plant W J and Farquharson G. 2012. Origins of features in wave number-frequency spectra of space-time images of the ocean. Journal of Geophysical Research: Oceans, 117(C6): C06015
  40. 40.
    Plant W J and Keller W C. 1990. Evidence of Bragg scattering in microwave Doppler spectra of sea return. Journal of Geophysical Research: Oceans, 95(C9): 16299-16310
  41. 41.
    Plant W J, Keller W C and Hayes K. 2005. Measurement of river surface currents with coherent microwave systems. IEEE Transactions on Geoscience and Remote Sensing, 43(6): 1242-1257
  42. 42.
    Poulter E M, Smith M J and McGregor J A. 1994. Microwave backscatter from the sea surface: Bragg scattering by short gravity waves. Journal of Geophysical Research: Oceans, 99(C4): 7929-7943
  43. 43.
    Sergievskaya I A, Ermakov S A, Ermoshkin A V, Kapustin I A, Molkov A A, Danilicheva O A and Shomina O V. 2019. Modulation of dual-polarized X-band radar backscatter due to long wind waves. Remote Sensing, 11(4): 423
  44. 44.
    Stevens C L, Poulter E M, Smith M J and McGregor J A. 1999. Nonlinear features in wave-resolving microwave radar observations of ocean waves. IEEE Journal of Oceanic Engineering, 24(4): 470-480
  45. 45.
    Tibshirani R. 2011. Regression shrinkage and selection via the lasso: a retrospective. Journal of the Royal Statistical Society Series B: Statistical Methodology, 73(3): 273-282
  46. 46.
    Wan Y, Ma E N, Qu R Z and Dai Y S. 2023. Accuracy evaluation of wave spectrum inversion based on Sentinel-1 and GF-3 SAR data. National Remote Sensing Bulletin, 27(4): 891-904
  47. 47.
    Wang C L and Liu L P. 2021. Analysis of the average price of second-hand houses in shanghai based on gradient boosting regression tree. Operations Research and Fuzziology, 11(3): 257-267
  48. 48.
    Wu S T, Zhao C, Chen Z Z, Xu Q H and Wang X. 2023. Wave parameter inversion from motion-affected echoes using shipboard coherent microwave radar. IEEE Transactions on Geoscience and Remote Sensing, 61: 5106311
  49. 49.
    Young I R, Rosenthal W and Ziemer F. 1985. A three-dimensional analysis of marine radar images for the determination of ocean wave directionality and surface currents. Journal of Geophysical Research: Oceans, 90(C1): 1049-1059
  50. 50.
    Zhao C, Wang X, Chen Z Z, Wu S T and Zeng Y C. 2023. Inversion of wave parameters from time-Doppler spectrum using shore-based coherent S-band radar. IEEE Transactions on Geoscience and Remote Sensing, 61: 5101211

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