星载GNSS反射信号台风检测及风眼位置估计

甄佳欢; 朱云龙; 杨东凯; 张国栋; 王峰

doi:10.13700/j.bh.1001-5965.2023.0395

星载GNSS反射信号台风检测及风眼位置估计

doi: 10.13700/j.bh.1001-5965.2023.0395

甄佳欢¹,
朱云龙¹,
杨东凯¹,
张国栋²,
王峰^{1, 3, ,}

1.
北京航空航天大学电子信息工程学院，北京 100191
2.
中国空间技术研究院通信与导航卫星总体部，北京 100094
3.
中国气象科学研究院灾害天气国家重点实验室，北京 100081

基金项目:

灾害天气国家重点实验室开放课题（2024LASW-B20）；博士后创新人才支持计划(BX20200039)

详细信息

通讯作者:
E-mail：wangf.19@163.com

中图分类号: TN967.1
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出版历程
- 收稿日期: 2023-06-19
- 录用日期: 2023-09-15
- 网络出版日期: 2023-10-16
- 整期出版日期: 2025-06-30

Detection of typhoons and estimation of eye position using satellite-based GNSS-reflectometry

1.
School of Electronic and Information Engineering，Beihang University，Beijing 100191，China
2.
Institute of Telecommunication and Navigation Satellites，China Academy of Space Technology，Beijing 100094，China
3.
State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081, China

Funds:

The Open Grants of the State Key Laboratory of Severe Weather (2024LASW-B20); China National Postdoctoral Program for Innovative Talents (BX20200039)

More Information

Corresponding author: E-mail：wangf.19@163.com

摘要

摘要:
全球导航卫星系统(GNSS)反射信号主要利用时延-多普勒图(DDM)的奇异性对台风进行研究。但由于模糊函数的存在，会使得这一奇异特征被模糊从而影响检测性能。通过将 DDM反卷积去除模糊函数的影响后再重构归一化散射系数(NBRCS)并将其映射到空间域，对台风事件检测算法及风眼位置估计算法进行研究。根据台风场内的DDM时间序列重构得到空间域NBRCS分布序列，定义台风敏感特征观测量，并基于置信区间距离半径的滑动窗口异常检测方法来检测台风事件；提出一种台风眼位置估计算法，通过模型匹配算法将重构所得空间域散射系数与模型数据集匹配以得到风眼位置。结果表明：台风检测算法的检测性能对信噪比(SNR)敏感，信噪比越高检测性能越好，当SNR为8时，台风检测性能接收机操作特性(ROC)曲线的曲线下面积(AUC)为0.80；在4种SNR下风眼位置估计的平均误差不超过0.3°，均方根误差不超过0.6°。
- 全球导航卫星系统反射测量技术 /
- 台风检测 /
- 台风中心 /
- 全球导航卫星系统 /
- 热带气旋
Abstract:
The singularity of the delay-Doppler map (DDM) is the primary focus of study on typhoons using global navigation satellite system (GNSS) reflectometry. However, due to the presence of ambiguity functions, this singularity is blurred, which affects detection performance. The normalized bistatic radar cross section (NBRCS) is reconstructed from deconvolving DDM and mapped to the spatial domain, based on which the detection of typhoon events and estimation algorithm of eye position is studied. The distribution sequence of NBRCS in the spatial domain can be obtained by reconstructing the DDM time series in the typhoon field. A detector of typhoon features is defined, and a sliding window anomaly detection method based on the confidence interval distance radius is used to detect typhoon events. A typhoon eye position estimation algorithm is proposed, which matches the reconstructed spatial scattering coefficient with the model dataset through a model-matching algorithm to obtain the eye position. The findings demonstrate how the signal-to-noise ratio (SNR) affects the typhoon detection algorithm's detection performance, with higher SNRs indicating greater detection performance. When the SNR is 8, the area under curve (AUC) of the receiver operating characteristic (ROC) curve of the typhoon detection performance is 0.80. The average deviation of the eye position estimation under four SNRs does not exceed 0.3°, and the root mean square deviation does not exceed 0.6°.
- global navigation satellite system-reflectometry /
- cyclone detector /
- cyclone center /
- CYGNSS /
- tropical cyclone

HTML全文

图 1 星载GNSS-R空间坐标系

Figure 1. Spaceborne GNSS-R space coordinate system

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图 2 海面TSM模型示意图

Figure 2. Schematic diagram of TSM model at sea

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图 3 空间观测范围相对台风风场的分布

Figure 3. Distribution of spatial observation range relative to tropical cyclone wind field

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图 4 台风风场和常规风场DDM分布

Figure 4. DDM distribution of tropical cyclone wind field and conventional wind field

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图 5 仿真场景下等时延线和等多普勒线的分布

Figure 5. Distribution of iso-delay lines and iso-Doppler lines in simulation scenarios

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图 6 反卷积结果

Figure 6. Result of deconvolution

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图 7 空间模糊问题

Figure 7. Problem of spatial ambiguity

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图 8 CYGNSS实测DDM

Figure 8. Actually measured DDM of CYGNSS

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图 9 空间域NBRCS映射结果

Figure 9. Mapping results of spatial domain NBRCS

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图 10 特征观测量$ {D_\sigma } $变化趋势

Figure 10. Changing tendency of $ {D_\sigma } $

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图 11 子序列置信区间距离半径变化

Figure 11. Changing tendency of subsequence confidence interval distance radius

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图 12 台风检测算法ROC曲线

Figure 12. ROC curve of typhoon detection algorithm

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图 13 模型匹配算法流程

Figure 13. Flow chart of model matching algorithm

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图 14 部分模型NBRCS示意图

Figure 14. Part of the model NBRCS schematic

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表 1 台风海况GNSS-R仿真参数

Table 1. GNSS-R simulation parameters of tropical cyclone

参数	描述	取值范围
\|T\|/m	GNSS卫星位置(CY02-DDMI2)
\|R\|/m	GNSS-R卫星位置(CY02-DDMI2)
\|v_t\|/(m·s⁻¹)	GNSS卫星速度(CY02-DDMI2)
\|v_r\|/(m·s⁻¹)	GNSS-R卫星速度(CY02-DDMI2)
P_t/W	GNSS发射功率	26.8
G_t/dB	GNSS发射天线增益	12.1
G_r/dB	GNSS-R接收天线增益	1
u₁₀/(m·s⁻¹)	台风风场风速
S/km	散射区	100×100
S_x,y/km	散射单元	1×1

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表 2 台风“山竹”参数

Table 2. Parameters of the tropical cyclone “Mangkhut”

台风名称	最大风速/(m·s⁻¹)	最大风速半径/km	台风眼位置
山竹	72.7	55.56	(124.3537°, 17.1511°)

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表 3 台风样本

Table 3. Tropical cyclone samples

序号	台风编号	台风名称
1	2018250N12170	台风“山竹”
2	2021197N19135	台风“烟花”
3	2021266N11149	台风“Mindulle”
4	2021222N12262	台风“Linda”
5	2019278N16165	超强台风“海贝思”
6	2018263N12146	台风“潭美”
7	2021172N11147	台风“Champi”
8	2020299N11144	超强台风“天鹅”
9	2019268N10155	台风“米娜”
10	2018263N12146	台风“玛莉亚”

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表 4 匹配结果

Table 4. Matching result

图像	对应风眼经度/(°)	对应风眼纬度/(°)	图像的均方根误差
图14(a)	124.4537	17.3511	51.48
图14(b)	124.0537	17.2511	49.45
图14(c)	124.2537	17.2511	48.73
图14(d)	124.3537	17.4511	52.64

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表 5 时序DDM联合估计结果

Table 5. Results of time series DDM joint estimation

台风	平均误差/(°)		均方根误差/(°)
台风	经度	纬度	经度	纬度
1	0.269	0.077	0.457	0.410
2	0.322	0.259	0.564	0.419
3	−0.075	0.575	0.141	0.591
4	0.257	0.000	0.470	0.800
5	−0.144	0.606	0.179	0.655
6	−0.275	0.100	0.534	0.660
7	−0.270	−0.530	0.395	0.269
8	−0.275	0.567	0.534	0.568
9	0.295	−0.195	0.542	0.380
10	0.490	0.148	0.715	0.415

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表 6 风眼估计结果误差绝对值与均方根误差随信噪比的变化

Table 6. Change of root mean square error and deviation absolute value with SNR

信噪比	误差绝对值/(°)		均方根误差/(°)
信噪比	经度	纬度	经度	纬度
SNR=8	0.275	0.100	0.534	0.269
SNR=6	0.125	0.338	0.534	0.411
SNR=4	0.113	0.138	0.627	0.582
SNR=2	0.638	0.663	0.649	0.664

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表 7 不同信噪比下风眼位置估计误差

Table 7. Estimation deviation of typhoon eye position under different SNR

信噪比	平均误差/(°)		均方根误差/(°)
信噪比	经度	纬度	经度	纬度
SNR=8	0.116	0.160	0.430	0.446
SNR=6	0.087	0.143	0.398	0.500
SNR=4	0.022	0.141	0.399	0.594
SNR=2	0.143	0.278	0.473	0.645

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