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An Approach for Filter Divergence Suppression in a Sequential Data Assimilation System and Its Application in Short-Term Traffic Flow Forecasting

1
College of Surveying and Geo-Informatics, Tongji University, No. 1239 Siping Road, Shanghai 200092, China
2
Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong 999077, China
3
Key Laboratory of Mineral Resources, Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing 100029, China
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2020, 9(6), 340; https://doi.org/10.3390/ijgi9060340
Received: 31 March 2020 / Revised: 21 May 2020 / Accepted: 25 May 2020 / Published: 26 May 2020
Mathematically describing the physical process of a sequential data assimilation system perfectly is difficult and inevitably results in errors in the assimilation model. Filter divergence is a common phenomenon because of model inaccuracies and affects the quality of the assimilation results in sequential data assimilation systems. In this study, an approach based on an L1-norm constraint for filter-divergence suppression in sequential data assimilation systems was proposed. The method adjusts the weights of the state-simulated values and measurements based on new measurements using an L1-norm constraint when filter divergence is about to occur. Results for simulation data and real-world traffic flow measurements collected from a sub-area of the highway between Leeds and Sheffield, England, showed that the proposed method produced a higher assimilation accuracy than the other filter-divergence suppression methods. This indicates the effectiveness of the proposed approach based on the L1-norm constraint for filter-divergence suppression. View Full-Text
Keywords: sequential data assimilation system; filter divergence; gain matrix; L1-norm constrained; short-term traffic flow forecasting sequential data assimilation system; filter divergence; gain matrix; L1-norm constrained; short-term traffic flow forecasting
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MDPI and ACS Style

Tong, X.; Wang, R.; Shi, W.; Li, Z. An Approach for Filter Divergence Suppression in a Sequential Data Assimilation System and Its Application in Short-Term Traffic Flow Forecasting. ISPRS Int. J. Geo-Inf. 2020, 9, 340. https://doi.org/10.3390/ijgi9060340

AMA Style

Tong X, Wang R, Shi W, Li Z. An Approach for Filter Divergence Suppression in a Sequential Data Assimilation System and Its Application in Short-Term Traffic Flow Forecasting. ISPRS International Journal of Geo-Information. 2020; 9(6):340. https://doi.org/10.3390/ijgi9060340

Chicago/Turabian Style

Tong, Xiaohua; Wang, Runjie; Shi, Wenzhong; Li, Zhiyuan. 2020. "An Approach for Filter Divergence Suppression in a Sequential Data Assimilation System and Its Application in Short-Term Traffic Flow Forecasting" ISPRS Int. J. Geo-Inf. 9, no. 6: 340. https://doi.org/10.3390/ijgi9060340

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