Research on BeiDou full-band GNSS-R water level inversion
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摘要: 北斗三号卫星导航系统(BDS-3)拥有丰富的频段数据，使得该系统在全球卫星导航反射测量(GNSS-R)领域具有广阔的应用前景. 本文系统地开展了北斗全频段GNSS-R水位反演研究，首先对比了频谱分析和非线性拟合两种算法在水位反演中的性能，结果表明非线性拟合的整体精度优于频谱分析；然后针对反演结果中存在的各种误差，引入Savitzky-Golay滤波算法去噪，数值表明去噪前后的结果与实测水位之差的均方根误差(RMSE)平均降低了7.4 cm，且北斗全频段的反演精度与GPS L5相当.
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关键词:
- GNSS-R /
- 水位反演 /
- 北斗卫星导航系统(BDS) /
- 频谱分析 /
- 非线性拟合 /
- Savitzky-Golay滤波
Abstract: China's BeiDou-3 (BDS-3) has abundant frequency band data, which makes the system promising for application in the field of Global Navigation Satellite System Reflectometry (GNSS-R). In this paper, we systematically carry out the study of BDS-R water level inversion, firstly, we compare the performance of two algorithms of spectral analysis and nonlinear fitting in water level inversion, and the results show that the overall accuracy of nonlinear fitting is better than that of spectral analysis; then, we introduce Savitzky-Golay filtering algorithm to denoise the inversion results for various errors, and the numerical values show that the difference between the results before and after denoising and the measured water level The average RMSE of the difference between the results before and after denoising and the measured water level is reduced by 7.4 cm, and the inversion accuracy of the full frequency band of BeiDou is comparable to that of GPS L5.-
Key words:
- GNSS-R /
- water level inversion /
- BeiDou system /
- spectral analysis /
- nonlinear fitting /
- Savitzky-Golay filtering
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表 1 北斗全频段水位反演结果统计信息
方法 波段 相关系数 RMSE/cm 反演点数量 频谱分析 L5 0.726 14.7 6235 B1C 0.786 12.5 7568 B1 0.747 13.9 7886 B2a 0.659 17.6 9805 B3 0.889 8.0 11641 B2b 0.708 15.8 11213 B2a+B2b 0.728 14.5 7564 非线性拟合 L5 0.760 13.7 5983 B1C 0.746 13.9 7986 B1 0.717 15.2 8321 B2a 0.750 14.6 9907 B3 0.893 8.2 12940 B2b 0.778 13.3 11877 B2a+B2b 0.820 11.8 7167 
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表 2 GNSS-R滤波前后精度统计
方法 波段 相关系数 RMSE/cm 滤波前 L5 0.760 13.7 B1C 0.746 13.9 B1 0.717 15.2 B2a 0.750 14.6 B3 0.893 8.2 B2b 0.778 13.3 B2a+B2b 0.820 11.8 滤波后 L5 0.936 6.0 B1C 0.934 6.0 B1 0.927 6.6 B2a 0.948 5.6 B3 0.968 4.3 B2b 0.950 5.4 B2a+B2b 0.961 4.8
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