Data-Driven Spatio-Temporal Modeling Using the Integro-Difference Equation

被引：24
|
作者：
Dewar, Michael [1 ]
Scerri, Kenneth [2 ]
Kadirkamanathan, Visakan [2 ]
机构：
[1] Univ Edinburgh, Sch Informat, Edinburgh EH8 9AB, Midlothian, Scotland
[2] Univ Sheffield, Dept Automat Control & Syst Engn, Sheffield S1 3JD, S Yorkshire, England
来源：
IEEE TRANSACTIONS ON SIGNAL PROCESSING | 2009年 / 57卷 / 01期
关键词：
Dynamic spatio-temporal modeling; expectation-maximization (EM) algorithm; Integro-Difference Equation (IDE); maximum-likelihood parameter estimation; state-space; MAXIMUM-LIKELIHOOD; IDENTIFICATION; DISPERSAL;
D O I：
10.1109/TSP.2008.2005091
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
A continuous-in-space, discrete-in-time dynamic spatio-temporal model known as the Integro-Difference Equation (IDE) model is presented in the context of data-driven modeling. A novel decomposition of the IDE is derived, leading to state-space representation that does not couple the number of states with the number of observation locations or the number of parameters. Based on this state-space model, an expectation-maximization (EM) algorithm is developed in order to jointly estimate the IDE model's spatial field and spatial mixing kernel. The resulting modeling framework is demonstrated on a set of examples.
引用
收藏
页码：83 / 91
页数：9
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