一种改进的局部线性回归估计器及其在雷达高度计海况偏差估计中的应用

蒋茂飞; 许可; 刘亚龙; 王磊

doi:10.11999/JEIT151280

一种改进的局部线性回归估计器及其在雷达高度计海况偏差估计中的应用

doi: 10.11999/JEIT151280

计量
- 文章访问数: 1293
- HTML全文浏览量: 84
- PDF下载量: 252
- 被引次数: 0
出版历程
- 收稿日期: 2015-11-17
- 修回日期: 2016-05-05
- 刊出日期: 2016-09-19

Improved Local Linear Regression Estimator and Its Application to Estimation for Radar Altimeter Sea State Bias

摘要

摘要: 在建立雷达高度计海况偏差(Sea State Bias, SSB)非参数模型时，通常会用到局部线性回归(Local Linear Regression, LLR)估计器，而传统的局部线性回归估计器涉及高维矩阵运算，当建模的数据量较大时，估计海况偏差需要大量的时间，从而使得非参数估计方法很难用于高维海况偏差模型。该文提出一种改进的局部线性回归(Improved Local Linear Regression, ILLR)估计器，可以避免传统的LLR估计器所需的高维矩阵运算，在不影响海况偏差估计结果的条件下，将局部线性回归估计器获取加权函数的时间复杂度由O(N2)降低为O(N)，从而大幅地降低估计海况偏差所需的时间，为实现高维非参数海况偏差模型的实时运算奠定了基础。
- 雷达高度计 /
- 海况偏差 /
- 非参数估计 /
- LLR估计器
Abstract: The Local Linear Regression (LLR) estimator is usually used when developing a nonparametric model for radar altimeter Sea State Bias (SSB). However, the conventional LLR estimator contains matrices with high dimension. When a large number of data are used to develop the SSB model, the SSB estimation costs too much time. Therefore, the nonparametric estimation method can hardly be used to develop high-dimensional SSB models. This paper presents an Improved LLR (ILLR) estimator, complexity fromO(N2)toO(N) which can avoid high-dimensional matrix operations. The improved LLR estimator can greatly reduce the time for SSB estimation without affecting the estimated accuracy. So the improved LLR estimator can laid the foundation for high-dimensional SSB models.
- Radar altimeter /
- Sea state bias /
- Nonparametric estimation /
- Local Linear Regression (LLR) estimator

HTML全文

参考文献(20)

王磊. 高精度卫星雷达高度计数据处理技术研究[D]. [博士论文], 中国科学院(国家空间科学中心), 2015.

WANG L. Study on the data processing for high precision satellite radar altimeter[D]. [Ph.D. dissertation], National Space Science Center, Chinese Academy of Sciences, 2015.

王磊, 许可, 史灵卫, 等. 一种消除合成孔径雷达高度计延迟校正中残余误差的新算法及仿真验证[J]. 电子与信息学报, 2015, 37(11): 2713-2718. doi: 10.11999/JEIT150282.

WANG L, XU K, SHI L W, et al. A new range migration correction algorithm and its simulation for SAR altimeter[J]. Journal of Electronics Information Technology, 2015, 37(11): 2713-2718. doi: 10.11999/JEIT150282.

刘亚龙. HY-2雷达高度计海面高度定标技术研究[D]. [博士论文], 中国海洋大学, 2014.

LIU Y L. Calibration technology for HY-2 radar altimeter sea surface height[D]. [Ph.D. dissertation], Ocean University of China, 2014.

GASPAR P, OGOR F, LETRAON P Y, et al. Estimating the sea-state bias of the topex and poseidon altimeters from crossover differences[J]. Journal of Geophysical Research- Oceans, 1994, 99(C12): 24981-24994. doi: 10.1029/ 94JC01430.

PRANDI P, PHILIPPS S, PIGNOT V, et al. SARAL/AltiKa global statistical assessment and cross-calibration with Jason-2[J]. Marine Geodesy, 2015, 38(10): 297-312. doi: 10.1080/01490419.2014.995840.

TRAN N, VANDEMARK D, CHAPRON B, et al. New models for satellite altimeter sea state bias correction developed using global wave model data[J]. Journal of Geophysical Research-Oceans, 2006, 111(C9): 141-152. doi: 10.1029/2005JC003406.

BONNEFOND P, EXERTIER P, LAURAIN O, et al. SARAL/AltiKa absolute calibration from the multi-mission corsica facilities[J]. Marine Geodesy, 2015, 38(10): 171-192. doi: 10.1080/01490419.2015.1029656.

GASPAR P and FLOREANS J P. Estimation of the sea state bias in radar altimeter measurements of sea level: Results from a new nonparametric method[J]. Journal of Geophysical Research-Oceans, 1998, 103(C08): 15803-15814. doi: 10.1029/ 98JC01194.

CHELTON D B. The sea-state bias in altimeter estimates of sea-level from collinear analysis of topex Data[J]. Journal of Geophysical Research-Oceans, 1994, 99(C12): 24995-25008. doi: 10.1029/94JC02113.

GASPAR P, LABROUE S, OGOR F, et al. Improving nonparametric estimates of the sea state bias in radar altimeter measurements of sea level[J]. Journal of Atmospheric and Oceanic Technology, 2002, 19(10): 1690-1707. doi: 10.1175/ 1520-0426(2002)0191690:INEOTS2.0.CO;2.

WANG J X and XIAO Q X. Local polynomial estimation of time-dependent diffusion parameter for discretely observed SDE models[J]. Filomat, 2014, 28(4): 871-878. doi: 10.2298/ FIL1404871W.

SU L and ULLAH A. Local polynomial estimation of nonparametric simultaneous equations models[J]. Journal of Econometrics, 2008, 144(1): 193-218. doi: 10.1016/j.jeconom. 2008.01.002.

DETTMERING D, SCHWATKE C, and BOSCH W. Global calibration of SARAL/AltiKa using multi-mission sea surface height crossovers[J]. Marine Geodesy, 2015, 38(10): 206-218. doi: 10.1080/01490419.2014.988832.

PAIGE C C and SAUNDERS M A. LSQR: an algorithm for sparse linear-equations and sparse least-squares[J]. ACM Transactions on Mathematical Software, 1982, 8(1): 43-71. doi: 10.1145/355984.355989.

FONG D C L and SAUNDERS M. LSMR: An iterative algorithm for sparse least-squares problems[J]. SIAM Journal on Scientific Computing, 2010, 33(5): 2950-2971. doi: 10.1137 /10079687X.

TRAN N, VANDEMARK D, LABROUE S, et al. Sea state bias in altimeter sea level estimates determined by combining wave model and satellite data[J]. Journal of Geophysical Research-Oceans, 2010, 115(C03020): 1-7. doi: 10.1029/ 2009JC005534.

FENG H, YAO S, LI L Y, et al. Spline-based nonparametric estimation of the altimeter sea-state bias correction[J]. IEEE Geoscience and Remote Sensing Letters, 2010, 7(3): 577-581. doi: 10.1109/LGRS.2010.2041894.

施引文献

资源附件(0)

访问统计