Retrieval of Ocean Wind Speed Using Airborne Reflected GNSS Signals
Retrieval of Ocean Wind Speed Using Airborne Reflected GNSS Signals
复制标题
使用机载反射 GNSS 信号反演海洋风速
DOI：
10.1109/access.2019.2915193
复制
发表时间：
2019
期刊：
影响因子：
3.9
通讯作者：
Li Xiaohui
中科院分区：
文献类型：
--
作者：
Gao Hongxing;Yang Dongkai;Wang Feng;Wang Qiang;Li Xiaohui
本文发展了可用于不同飞行场景的模型，利用归一化延迟波形(NDW)的宽度来反演风速。首先，分析了风速、风向、飞行高度、仰角等因素对NDW宽度的影响。探讨了各自变量对回归的贡献。结果表明，风速、飞行高度和仰角的贡献比风向的贡献更大，因此在模型中可以忽略风向。提出了以西北偏东向宽度、高度、仰角等函数项为自变量，以风速为因变量的多元回归方法，建立了风速反演模型。通过仿真，可以得到3m/S以上的均方根误差。为了提高检索性能，训练了BP网络作为上述分析模型的替代。在与解析模型相同的条件下，均方根误差小于2.5m/S，取得了较好的效果。对风速反演的误差进行了分析。结果表明：1)解析模型和BP网络都有其固有的回归偏差，特别是在风速为15~20m/S的高风速下，当风速大于16m/S时，反演精度有急剧下降的趋势；最后，利用国家环境预报中心(NCEP)气候预报系统(CFS)提出的风速模型和现场风速比较的匹配方法，对机载风速数据进行了处理，以求取风速。通过对检索结果的比较，本文提出的方法可以获得与相同水平的匹配方法相同的准确率。
This paper develops the models that can be used in different flight scenarios to retrieve the wind speed using the width of the normalized delay waveform (NDW). First, the factors that influence the NDW width, including the wind speed, wind direction, flight height, and elevation angle, are analyzed. The contribution of each independent variable to the regression is explored. The results show that the wind speed, flight height, and elevation angle contribute more significantly than the wind direction so the wind direction can be ignored in the model. The multiple regression, in which the function terms of NWD width, flight height, and elevation angle above are taken as independent variables and wind speed is taken as the dependent variable, is proposed to develop the model of retrieving wind speed. Through the simulation, a root-mean-square error (RMSE) over 3 m/s can be obtained. In order to improve the retrieval performance, a Back-Propagation (BP) network is trained as an alternative to the analytical models above. Better performance is achieved with an RMSE less than 2.5 m/s under the same conditions with the analytical model. The errors of retrieved wind speed are analyzed. The conclusions are that: 1) both analytical model and BP network have inherent regression biases, especially for high wind speed from 15 to 20m/s so that at wind speeds higher than 16 m/s, the tendency for retrieval accuracy to rapidly become worse appears and 2) the number of incoherent averaging should be over 1000 to reduce the impact of thermal and speckle noise. At the end of the paper, airborne data are processed to retrieve wind speed utilizing proposed models and the matching method to compare in-situ wind speed from the National Centers for Environmental Prediction (NCEP) Climate Forecast System (CFS). By comparing the retrieval results, the proposed methods could obtain the accuracy with the same level of matching method.