雷达高度计海况偏差估计神经网络模型研究

郭迎婷; 苗洪利; 张国首; 荆玉洁; 王桂忠

doi:10.3969/j.issn.0253-4193.2017.07.012

雷达高度计海况偏差估计神经网络模型研究

doi: 10.3969/j.issn.0253-4193.2017.07.012

中国海洋大学信息科学与工程学院, 山东青岛 266100

基金项目: 国家自然科学基金"雷达高度计海况偏差校正综合模型研究"（41176157）；国家自然科学青年基金"降雨条件下HY-2高度计有效波高反演技术研究"（41406197）；海洋环境安全保障重点专项"三维成像雷达高度计海洋信息提取技术及应用（2016YFC1401004）。

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出版历程
- 收稿日期: 2016-09-05
- 修回日期: 2016-12-05

Study on neural network model of estimating the sea state bias for radar altimeters

College of Information Science and Engineering, Ocean University of China, Qingdao 266100, China

摘要

摘要: 本文基于Jason-2高度计数据，在12个不同季节的cycle数据中组合1~6个cycle的有效波高、风速和海况偏差为训练集，选取Jason-2的另外3个不同季节的cycle数据集为测试集。经检验分析，确定3个cycle对应的BP神经网络模型。将该模型应用于HY-2高度计海况偏差的估计，通过海况偏差与有效波高及风速的拟合优度、解释方差和残差对比分析，结果表明：神经网络BP模型可以有效应用于HY-2的海况偏差估计并明显优于传统海况偏差参数模型。
- 雷达高度计 /
- 海况偏差 /
- 神经网络模型 /
- 有效波高 /
- 风速
Abstract: In this paper, which is based on the Jason-2 altimeter data, with the data of the significant wave height (SWH), wind speed (U) and sea state bias (SSB) combination of 1-6 cycle in 12 different seasons in the cycle data as the training set, select the other 3 cycles of Jason-2 data as the test set. By the test analysis, the BP neural network model which corresponds to 3 cycles for estimating the SSB is established. The model is applied to the estimations of SSB in the HY-2 altimeter, and the performances of the model can be evaluated by the goodness of fit between U and SWH by SSB, explained variance and residual contrast analysis. It suggests that the BP neutral network model can be effectively applied to the HY-2 estimations of SSB and significantly better than the traditional parameter model of sea state bias.
- radar altimeter /
- sea state bias /
- neural network model /
- wave height /
- wind speed

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参考文献(13)

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