An LSTM based Kalman Filter for Spatio-temporal Ocean Currents Assimilation
An LSTM based Kalman Filter for Spatio-temporal Ocean Currents Assimilation
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基于 LSTM 的时空洋流同化卡尔曼滤波器
DOI：
10.1145/3366486.3366522
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发表时间：
2019
期刊：
影响因子：
--
通讯作者：
Edwards, Catherine R.
中科院分区：
文献类型：
--
作者：
Zhang, Ziqiao;Hou, Mengxue;Zhang, Fumin;Edwards, Catherine R.
在本文中，我们提出了一个基于长短期记忆（LSTM）的卡尔曼滤波器的数据同化的二维时空变化的深度平均海洋流场的水下滑翔机路径规划。过滤器的数据源将欧拉流图与拉格朗日移动的传感器数据流相结合。深度平均流被建模为两个组件，潮汐和非潮汐流组件。潮汐流场采用ADCIRC（Advanced Three-Dimensional Circulation Model）模型，非潮汐流场采用空间基函数及其时间序列系数模型。空间基函数是通过对高频雷达（HFR）测量的历史表面流场进行经验正交函数（Empirical Orthogonal Functions，ERF）分析得出的主要模式，空间基函数的时间系数由LSTM神经网络建模。执行卡尔曼滤波器以联合收割机从LSTM网络导出的动态和来自滑翔机流估计数据的观测。数值模拟结果表明，本文提出的数据同化方法可以提供合理的流场预报精度。
In this paper, we present a Long Short-Term Memory (LSTM)-based Kalman Filter for data assimilation of a 2D spatio-temporally varying depth-averaged ocean flow field for underwater glider path planning. The data source to the filter combines both the Eulerian flow map with the Lagrangian mobile sensor data stream. The depth-averaged flow is modeled as two components, the tidal and the non-tidal flow component. The tidal flow is modeled with ADCIRC (Advanced Three-Dimensional Circulation Model), while the non-tidal flow field is modeled by a set of spatial basis functions and their time series coefficients. The spatial basis functions are the principal modes derived by performing EOF (Empirical Orthogonal Functions) analysis on the historical surface flow field measured by high frequency radar (HFR), and the temporal coefficients of the spatial basis function are modeled by an LSTM neural network. The Kalman Filter is performed to combine the dynamics derived from the LSTM network, and the observations from the glider flow estimation data. Simulation results demonstrate that the proposed data assimilation method can give flow field prediction of reasonable accuracy.
基于神经网络的卡尔曼滤波器用于卫星海面温度的时空插值
DOI：
10.3390/rs10121864
发表时间：
2018
期刊：
Remote. Sens.
影响因子：
--
作者：
Said Ouala;Ronan Fablet;C. Herzet;B. Chapron;A. Pascual;F. Collard;L. Gaultier
通讯作者：
L. Gaultier
影响因子：
4.7
作者：
Salti, Samuele;Di Stefano, Luigi
通讯作者：
Di Stefano, Luigi
影响因子：
4.1
作者：
Szwaykowska, Klementyna;Zhang, Fumin
通讯作者：
Zhang, Fumin