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Data-driven robust state estimation based on EK-SVSF
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AI
DOI：10.1016/j.neucom.2026.132869.png)
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Abstract
En 中文
This paper introduces a novel extension to the Extended Kalman-based Smooth Variable Structure Filter (EK-SVSF), a hybrid state estimation framework that integrates the Extended Kalman Filter (EKF) with the Smooth Variable Structure Filter (SVSF). Tailored for nonlinear systems subject to model uncertainties and external disturbances, EK-SVSF enhances estimation accuracy by leveraging the complementary strengths of its constituent filters. Nonetheless, the efficacy of EK-SVSF hinges critically on the selection of an appropriate width for the smoothing boundary layer (SBL); suboptimal values—either excessively large or small—can substantially impair filtering performance. Compounding this issue, inherent model uncertainties render the determination of an optimal SBL a formidable and enduring challenge. To mitigate this, we propose a data-driven methodology that autonomously extracts salient features from the smoothing boundary function, thereby resolving the parameter tuning dilemma under model uncertainty. Furthermore, to refine the associated multi-loss weighted aggregation, we incorporate an adaptive weighting scheme based on the coefficient of variation, enabling dynamic optimization. Empirical evaluations demonstrate that the proposed approach yields robust and resilient state estimation outcomes, even in the presence of significant model discrepancies.
Keywords:
EK-SVSF
state estimation
smoothing boundary layer
model uncertainty
data-driven methodology
Journal
IF:
6.5
Papers:
2.5W
Citations:
6.5W