Please use this identifier to cite or link to this item: https://doi.org/10.1109/AERO.2018.8396430
Title: An Inequality Constrained Ensemble Kalman Filter for Parameter Estimation Application
Authors: Goh, Shu Ting 
Soon, Jing Jun 
Low, Kay-Soon
Keywords: Science & Technology
Technology
Engineering, Aerospace
Engineering
DATA ASSIMILATION
Issue Date: 1-Jan-2018
Publisher: IEEE
Citation: Goh, Shu Ting, Soon, Jing Jun, Low, Kay-Soon (2018-01-01). An Inequality Constrained Ensemble Kalman Filter for Parameter Estimation Application. IEEE Aerospace Conference 2018-March. ScholarBank@NUS Repository. https://doi.org/10.1109/AERO.2018.8396430
Abstract: This paper proposes a Selective Constrained Ensemble Kalman Filter (SCenKF) for nonlinear parameter estimation application. The ensemble based Kalman filter has two advantages. Firstly, its stability performance is not affected by large initial state estimation error. Secondly, the nonlinear model does not require to be linearized. The inequality constraints are represented in terms of error function and complementary error function. In addition, the characteristics of particle filter and genetic algorithm are integrated into SCenKF to prevent matrix singular problem in Kalman gain computation and enable the memory mechanism on the historical best performance ensemble member. To study the performance of the proposed SCenKF, a case study for photovoltaic (PV) model parameter estimation is used. In this study, the proposed SCenKF is compared with the particle swarm optimization (PSO) and improved comprehensive photovoltaic parameter identification (ICPPI) method. The result shows that the proposed SCenKF achieves better estimation accuracy as compared to the other two methods. In addition, the computational cost of the SCenKF falls between PSO and ICPPI.
Source Title: IEEE Aerospace Conference
URI: https://scholarbank.nus.edu.sg/handle/10635/231059
ISBN: 9781538620144
ISSN: 1095-323X
DOI: 10.1109/AERO.2018.8396430
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