Sage–Husa Algorithm Based on Adaptive Double Forgetting Factors
Source abstract
Abstract
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.
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Research context
OpenAlex topics: Target Tracking and Data Fusion in Sensor Networks · Inertial Sensor and Navigation · Forecasting Techniques and Applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Marine Equipment and Technology Institute, Jiangsu University of Science and Technology, Zhenjiang 212003, China
School of Naval Architecture and Ocean Engineering, Jiangsu University of Science and Technology, Zhenjiang 212003, China
Key Laboratory of High Performance Ship Technology, Ministry of Education, Wuhan University of Technology, Wuhan 430063, China
Funding
National Natural Science Foundation of China
References (43)
Show source-provided references
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