Sage–Husa Algorithm Based on Adaptive Double Forgetting Factors

In order to address the issues of insufficient filtering accuracy and filtering divergence that have been observed in the Sage–Husa algorithm when applied to nonlinear system state estimation, an adaptive double forgetting factor-based Sage–Husa algorithm is proposed. This algorithm builds upon the...

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Main Authors: Wenjuan Li, Mingjing Zhan, Hui Feng
Format: Article
Language:English
Published: MDPI AG 2025-02-01
Series:Applied Sciences
Subjects:
Online Access:https://www.mdpi.com/2076-3417/15/4/1731
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author Wenjuan Li
Mingjing Zhan
Hui Feng
author_facet Wenjuan Li
Mingjing Zhan
Hui Feng
author_sort Wenjuan Li
collection DOAJ
description In order to address the issues of insufficient filtering accuracy and filtering divergence that have been observed in the Sage–Husa algorithm when applied to nonlinear system state estimation, an adaptive double forgetting factor-based Sage–Husa algorithm is proposed. This algorithm builds upon the Sage–Husa algorithm with forgetting factors by introducing double forgetting factors and adaptively adjusting them using a windowing method combined with an exponential form. On the basis of ensuring the semi-positive definiteness of the process noise covariance matrix and the positive definiteness of the observation noise covariance matrix, a covariance matching technique is employed to determine whether the measurement noise statistical characteristics need to be re-updated. The results of the simulations demonstrate that the proposed algorithm enhances the accuracy of filtering and exhibits strong effectiveness and feasibility.
format Article
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publishDate 2025-02-01
publisher MDPI AG
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spelling doaj-art-012a47ab4d4e421da76b24884fc386402025-08-20T03:11:04ZengMDPI AGApplied Sciences2076-34172025-02-01154173110.3390/app15041731Sage–Husa Algorithm Based on Adaptive Double Forgetting FactorsWenjuan Li0Mingjing Zhan1Hui Feng2Marine Equipment and Technology Institute, Jiangsu University of Science and Technology, Zhenjiang 212003, ChinaSchool of Naval Architecture and Ocean Engineering, Jiangsu University of Science and Technology, Zhenjiang 212003, ChinaKey Laboratory of High Performance Ship Technology, Ministry of Education, Wuhan University of Technology, Wuhan 430063, ChinaIn order to address the issues of insufficient filtering accuracy and filtering divergence that have been observed in the Sage–Husa algorithm when applied to nonlinear system state estimation, an adaptive double forgetting factor-based Sage–Husa algorithm is proposed. This algorithm builds upon the Sage–Husa algorithm with forgetting factors by introducing double forgetting factors and adaptively adjusting them using a windowing method combined with an exponential form. On the basis of ensuring the semi-positive definiteness of the process noise covariance matrix and the positive definiteness of the observation noise covariance matrix, a covariance matching technique is employed to determine whether the measurement noise statistical characteristics need to be re-updated. The results of the simulations demonstrate that the proposed algorithm enhances the accuracy of filtering and exhibits strong effectiveness and feasibility.https://www.mdpi.com/2076-3417/15/4/1731nonlinear systemSage–Husaadaptive double forgetting factorswindowingexponential form
spellingShingle Wenjuan Li
Mingjing Zhan
Hui Feng
Sage–Husa Algorithm Based on Adaptive Double Forgetting Factors
Applied Sciences
nonlinear system
Sage–Husa
adaptive double forgetting factors
windowing
exponential form
title Sage–Husa Algorithm Based on Adaptive Double Forgetting Factors
title_full Sage–Husa Algorithm Based on Adaptive Double Forgetting Factors
title_fullStr Sage–Husa Algorithm Based on Adaptive Double Forgetting Factors
title_full_unstemmed Sage–Husa Algorithm Based on Adaptive Double Forgetting Factors
title_short Sage–Husa Algorithm Based on Adaptive Double Forgetting Factors
title_sort sage husa algorithm based on adaptive double forgetting factors
topic nonlinear system
Sage–Husa
adaptive double forgetting factors
windowing
exponential form
url https://www.mdpi.com/2076-3417/15/4/1731
work_keys_str_mv AT wenjuanli sagehusaalgorithmbasedonadaptivedoubleforgettingfactors
AT mingjingzhan sagehusaalgorithmbasedonadaptivedoubleforgettingfactors
AT huifeng sagehusaalgorithmbasedonadaptivedoubleforgettingfactors