Optimal smoothing for spherical Gauss-Markov Random Fields with application to weather data estimation

被引：4

作者：

Borri, Alessandro ^{[1
]}

Carravetta, Francesco ^{[2
]}

White, Langford B. ^{[3
]}

机构：

[1] CNR, Biomath Lab, Ist Anal Sistemi & Informat Antonio Ruberti, Rome, Italy

[2] CNR, Ist Anal Sistemi & Informat Antonio Ruberti, Rome, Italy

[3] Univ Adelaide, Sch Elect & Elect Engn, Adelaide, SA 5005, Australia

来源：

EUROPEAN JOURNAL OF CONTROL | 2017年 / 33卷

关键词：

Random fields; Estimation and filtering; Optimal smoothing; Weather data; Geophysical signal processing; SYSTEMS;

D O I：

10.1016/j.ejcon.2016.11.001

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper considers the smoothing problem for inhomogeneous Gauss-Markov Random Fields on a spherical lattice. Various observation models are considered, such as the case of noisy, possibly correlated, observations available only on a subset of sites, or a variable number of process components being measured. A 2D recursive optimal smoothing algorithm is derived, with computational complexity of O(N-2) where N is the number of sites, in line with known more common algorithms for inhomogeneous fields on rectangular lattices. An application of the method in weather forecasting using real data is presented, showing the capability of the proposed method. (C) 2016 European Control Association. Published by Elsevier Ltd. All rights reserved.

引用

页码：43 / 51

页数：9

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