基于BP神经网络的高频地波雷达海流空间插值
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摘要
高频地波雷达是海洋环境监测的重要手段,当前已经实现对海流的业务化观测,但是外部因素常引起海流空间探测的不连续性。为解决此问题,尽量保障区域数据的完整性和准确性,本文将BP神经网络技术与空间插值相结合,建立了海流的BP神经网络插值模型,并进行了针对实测数据的缺失插值仿真,通过与反距离权重法和线性插值法插值结果的对比,分析该模型在区域海流大面积缺失、流速整体较大和流速整体较小3个方面的性能。结果表明,BP神经网络插值模型的海流预测效果明显优于其他两种方法,且在流场数据大范围缺失下也取得了良好的效果。
High frequency(HF) ground wave radar is an important means of sea monitoring, HF radar routine observations of sea current have been operated for decades of years. Gaps in current data often occur due to external interferences. In order to ensure the integrity and accuracy of regional data, a back propagation(BP) neutral network interpolation model for ocean current is established by combining BP network technology and spatial interpolation. Two other interpolation methods, inverse distance weighted method and linear interpolation method, are adopted for comparison to validate the performance of the BP neutral network interpolation model. Simulations are conducted to analysis the performance of this new model in the cases of large areas of ocean current loss, large current velocity and relatively small current velocity. The results show that the prediction effect of the BP neutral network method is obviously better than the other two methods, and the new model also achieves good results in the absence of a wide range of current.
High frequency(HF) ground wave radar is an important means of sea monitoring, HF radar routine observations of sea current have been operated for decades of years. Gaps in current data often occur due to external interferences. In order to ensure the integrity and accuracy of regional data, a back propagation(BP) neutral network interpolation model for ocean current is established by combining BP network technology and spatial interpolation. Two other interpolation methods, inverse distance weighted method and linear interpolation method, are adopted for comparison to validate the performance of the BP neutral network interpolation model. Simulations are conducted to analysis the performance of this new model in the cases of large areas of ocean current loss, large current velocity and relatively small current velocity. The results show that the prediction effect of the BP neutral network method is obviously better than the other two methods, and the new model also achieves good results in the absence of a wide range of current.
引文
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[2] Li Miao,Wu Xiongbin,Zhang Lan,et al.A new algorithm for surface currents inversion with high-frequency over-the-horizon radar[J].IEEE Geoscience and Remote Sensing Letters,2017,14(8):1303-1307.
[3] Lipa B,Barrick D.Least-squares methods for the extraction of surface currents from CODAR crossed-loop data:application at ARSLOE[J].IEEE Journal of Oceanic Engineering,1983,8(4):226-253.
[4] Gurgel K W,Antonischki G,Essen H H,et al.Wellen radar (WERA):a new ground-wave HF radar for ocean remote sensing[J].Coastal Engineering,1999,37(3/4):219-234.
[5] Zhang Lan,Wu Xiongbin,Liu Jianfei,et al.A study on hybrid networking system for distributed HF over-the-horizon radars[C]//Proceedings of 2014 XXXIth URSI General Assembly and Scientific Symposium.Beijing,China:IEEE,2014:1-4.
[6] Barrick D E,Evans M W,Weber B L.Ocean surface currents mapped by radar[J].Science,1977,198(4313):138-144.
[7] 董志南,郑拴宁,赵会兵,等.基于空间插值的风场模拟方法比较分析[J].地球信息科学学报,2015,17(1):37-44.Dong Zhinan,Zheng Shuanning,Zhao Huibing,et al.Comparative analysis of methods of wind field simulation based on spatial Interpolation[J].Journal of Geo-Information Science,2015,17(1):37-44.
[8] Ragnoli E,Zhuk S,Donncha F O,et al.An optimal interpolation scheme for assimilation of HF radar current data into a numerical ocean model[C]//Proceedings of 2012 Oceans.Hampton Roads,VA,USA:IEEE,2012:1-5.
[9] 姚春雨,陈章友,吴雄斌,等.一种应用于X波段海洋监测雷达的插值算法研究[J].科学技术与工程,2015,15(34):64-68.Yao Chunyu,Chen Zhangyou,Wu Xiongbin,et al.A study on interpolation algorithms applied to marine monitoring of X-band radar[J].Science Technology and Engineering,2015,15(34):64-68.
[10] 王天驹,齐琳琳,朱江,等.HY-2卫星高度计波高资料在集合最优插值同化中的应用研究——以台风“Lipee”为例[J].海洋学报,2017,39(2):29-38.Wang Tianju,Qi Linlin,Zhu Jiang,et al.Application studies of using HY-2 satellite altimeter wave data in ensemble optimal interpolation method-“Lipee” for instance[J].Haiyang Xuebao,2017,39(2):29-38.
[11] 李泰儒,彭晓春,郑国栋,等.反距离加权法流场矢量插值研究[J].华北水利水电学院学报,2009,30(2):12-14.Li Tairu,Peng Xiaochun,Zheng Guodong,et al.Research on interpolation for vectors of flow field with inverse distance weighted method[J].Journal of North China Institute of Water Conservancy and Hydroelectric Power,2009,30(2):12-14.
[12] 张燕,任安虎.多特征与BP神经网络车牌识别系统研究[J].科学技术与工程,2012,12(22):5645-5648.Zhang Yan,Ren Anhu.Research on multiple feature and BP neural network licence plate recognition system[J].Science Technology and Engineering,2012,12(22):5645-5648.
[13] 戚龙,赵丹.基于BP神经网络的非特定人语音识别算法[J].科学技术与工程,2017,17(31):277-282.Qi Long,Zhao Dan.Algorithm of speaker independent speech recognition based on BP neural network[J].Science Technology and Engineering,2017,17(31):277-282.
[14] Sun Xiao,Lv Man,Quan Changqin,et al.Improved facial expression recognition method based on ROI deep convolutional neutral network[C]//Proceedings of the 7th International Conference on Affective Computing and Intelligent Interaction.San Antonio,TX,USA:IEEE,2017:256-261.
[15] Zhang Yunbo,Lei Zhiguo,Li Shengzhu,et al.Research on BP neutral network based grid-connected photovoltaic inverter[C]//Proceedings of 2016 Chinese Control and Decision Conference.Yinchuan,China:IEEE,2016:506-509.
[16] 程家昌,黄鹏,熊昌盛,等.基于BP神经网络的土壤养分空间插值[J].广东农业科学,2013,40(7):64-67,71.Cheng Jiachang,Huang Peng,Xiong Changsheng,et al.Spatial prediction of soil nutrition based on BP neural network[J].Guangdong Agricultural Sciences,2013,40(7):64-67,71.
[17] 刘永锋,李润祥,李纯斌,等.BP神经网络和支持向量机在积温插值中的应用[J].干旱区资源与环境,2014,28(5):158-165.Liu Yongfeng,Li Runxiang,Li Chunbin,et al.Application of BP neural network and support vector machine to the accumulated temperature interpolation[J].Journal of Arid Land Resources and Environment,2014,28(5):158-165.
[18] 刘婷,邵景安.基于BP神经网络的三峡库区土壤侵蚀强度模拟[J].自然资源学报,2018,33(4):669-683.Liu Ting,Shao Jing’an.Simulation of soil erosion intensity in the Three Gorges Reservoir Area using BP neural network[J].Journal of Natural Resources,2018,33(4):669-683.
[19] 王静,唐军武,何宜军,等.X-波段雷达近海海浪频谱反演的神经网络模型[J].海洋学报,2013,35(2):43-51.Wang Jing,Tang Junwu,He Yijun,et al.The retrieval of a nearshore wave frequency spectrum with X-band radar based on neural network[J].Haiyang Xuebao,2013,35(2):43-51.
[20] 郭迎婷,苗洪利,张国首,等.雷达高度计海况偏差估计神经网络模型研究[J].海洋学报,2017,39(7):124-130.Guo Yingting,Miao Hongli,Zhang Guoshou,et al.Study on neural network model of estimating the sea state bias for radar altimeters[J].Haiyang Xuebao,2017,39(7):124-130.
[21] Barrick D.First-order theory and analysis of MF/HF/VHF scatter from the Sea[J].IEEE Transactions on Antennas and Propagation,1972,20(1):2-10.
[22] Widrow B,Mantey P E,Griffiths L J,et al.Adaptive antenna systems[J].Proceedings of the IEEE,1967,55(12):2143-2159.
[23] Schmidt R.Multiple emitter location and signal parameter estimation[J].IEEE Transactions on Antennas and Propagation,1986,34(3):276-280.
[24] Li Chuan,Wu Xiongbin,Yue Xianchang,et al.Extraction of wind direction spreading factor from broad-beam high-frequency surface wave radar data[J].IEEE Transactions on Geoscience and Remote Sensing,2017,55(9):5123-5133.
[25] Moré J J.The Levenberg-marquardt algorithm:implementation and theory[M]//Watson G A.Numerical Analysis.Berlin,Heidelberg:Springer,1978:105-116.