Sigma-point particle filter for parameter estimation in a multiplicative noise environment

被引:4
作者
Ambadan, Jaison Thomas [1 ,2 ]
Tang, Youmin [1 ,3 ]
机构
[1] Univ No British Columbia, Prince George, BC V2N 4Z9, Canada
[2] Max Planck Inst Meteorol, Int Max Planck Res Sch Earth Syst Modelling, Hamburg, Germany
[3] State Ocean Adm, State Key Lab Satellite Ocean Environm Dynam, Inst Oceanog 2, Hangzhou, Zhejiang, Peoples R China
来源
JOURNAL OF ADVANCES IN MODELING EARTH SYSTEMS | 2011年 / 3卷
基金
加拿大自然科学与工程研究理事会;
关键词
ADVANCED DATA ASSIMILATION; KALMAN FILTER; ENSEMBLE FILTER; MODEL; PREDICTION; ERROR; STATE; SYSTEM; IMPACT; CHAOS;
D O I
10.1029/2011MS000065
中图分类号
P4 [大气科学(气象学)];
学科分类号
0706 ; 070601 ;
摘要
A pre-requisite for the "optimal estimate'' by the ensemble-based Kalman filter (EnKF) is the Gaussian assumption for background and observation errors, which is often violated when the errors are multiplicative, even for a linear system. This study first explores the challenge of the multiplicative noise to the current EnKF schemes. Then, a Sigma Point Kalman Filter based Particle Filter (SPPF) is presented as an alternative to solve the issues associated with multiplicative noise. The classic Lorenz '63 model and a higher dimensional Lorenz '96 model are used as test beds for the data assimilation experiments. Performance of the SPPF algorithm is compared against a standard EnKF as well as an advanced square-root Sigma-Point Kalman Filters (SPKF). The results show that the SPPF outperforms the EnKF and the square-root SPKF in the presence of multiplicative noise. The super ensemble structure of the SPPF makes it computationally attractive compared to the standard Particle Filter (PF).
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页数:16
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