基于奇异谱分析的DGPS浮标海面高测量误差研究
Study on Denoising the Instrumental Errors of the Sea Surface Height Series Derived from Buoys
查看参考文献10篇
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文摘
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在高度计绝对定标实验中,利用差分GPS(Differential GPS,DGPS)浮标进行现场海面高测量得到的海面高(Sea Surface Height,SSH)序列之中,除了包含有由风浪引起的高频噪声外,还包括了仪器误差造成的影响。前者可通过低通滤波方式消除,而后者由于是全频段的白噪声,不能通过低通滤波的方式消除。通过研究奇异谱分析(Singular Spectrum Analysis,SSA)的方法,确定了嵌入维数和截断长度的选取准则,并对其进行仿真,验证了该方法在消除海面高序列的仪器误差噪声中的有效性,同时也验证了嵌入维数和截断长度选取准则的有效性。最后,利用该方法处理了某次DGPS浮标实验获得的海面高序列,处理结果与原始序列相比较为平滑,且在精度上有一定的改善,验证了奇异谱分析方法的有效性。 |
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其他语种文摘
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In absolute altimeter calibration campaigns,the Sea Surface Height(SSH)series derived from the in-situ Differential GPS(DGPS)buoys always include high frequency errors such as wind waves and instrumental noises. The former can be removed by low pass filters,while the latter can not,because the noises are white and can spread in the whole frequency band of interest.In this paper,the Singular Spectrum Analysis(SSA)method was applied to solve the problem,and select criteria of the embedding dimension and intercepting length.Simulations based on this innovative method were executed,the effectiveness in removing the instrumental noise and the selction criteria was validated.Finally,SSH series derived from an in-situ DGPS buoy experiment were processed by this method,and the study suggested that the processed series were smoother compared to the original series,and had some improvements in accuracy.Therefore,the validity of the SSA method was demonstrated. |
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来源
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遥感技术与应用
,2015,30(4):661-666 【核心库】
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DOI
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10.11873/j.issn.1004-0323.2015.4.0661
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关键词
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高度计定标
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海面高序列
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仪器误差
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奇异谱分析
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降噪
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地址
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中国科学院空间科学与应用研究中心, 中国科学院微波遥感技术重点实验室, 北京, 100190
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语种
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中文 |
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文献类型
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研究性论文 |
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ISSN
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1004-0323 |
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学科
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海洋学 |
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基金
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中国科学院国家空间科学中心五个重点培育方向
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文献收藏号
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CSCD:5513680
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参考文献 共
10
共1页
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1.
Chrstopher S W.
Satellite Altimeter Calibration and Validation Using GPS Buoy Technology,2005
|
被引
1
次
|
|
|
|
|
2.
张勇. 一种基于海面背景的WindSat极化通道替代定标方法研究.
遥感技术与应用,2014,29(5):727-734
|
被引
4
次
|
|
|
|
|
3.
Schoellhamer D H. Singular Spectrum Analysis for Time Series with Missing Data.
Geophysical Research Letters,2001,28(16):3187-3190
|
被引
17
次
|
|
|
|
|
4.
Alonso F J. Application of Singular Spectrum Analysis to the Smoothing of Raw Kinematic Signals.
Journal of Biomechanics,2005,38:1085-1092
|
被引
9
次
|
|
|
|
|
5.
Shu H C. Application of Singular Spectrum Analysis to Structural Monitoring and Damage Diagnosis of Bridges.
Structure and Infrastructure Engineering,2013:1-20
|
被引
1
次
|
|
|
|
|
6.
Xie J G. Singular Spectrum Analysis for Tropical Cylone Landing in Guangdong.
Journal of Tropical Meteorology,2003,9(2):201-206
|
被引
1
次
|
|
|
|
|
7.
吴洪宝. 奇异谱分析-最大熵预报方法.
甘肃气象,2000,18(1):1-5
|
被引
11
次
|
|
|
|
|
8.
刘元峰. 基于奇异谱分析的混沌序列降噪.
上海交通大学学报,2003,37(5):778-780
|
被引
12
次
|
|
|
|
|
9.
Hossein H. Singular Spectrum Analysis:Methodology and Comparison.
Journal of Data Science,2007,5:239-257
|
被引
3
次
|
|
|
|
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10.
Hossein H. Separability and Window Length in Singular Spectrum Analysis.
Computers Rendus Mathematique,2011,349(17):987-990
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被引
8
次
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