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Journals / Turkish Journal of Electrical Engineering and Computer Science / 2010 / Cilt: 18 - Sayı: 2

Stochastic stability of the discrete-time constrained extended Kalman filter

Levent ÖZBEK
Esin KÖKSAL BABACAN
Murat EFE
Pages
211–223
Publication Date
2010-02-01
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Stability results are verified and performance of the constrained EKF is demonstrated through simulations on a nonlinear engineering example.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:In this paper, stability of the projection-based constrained discrete-time extended Kalman filter (EKF) as applied to nonlinear systems in a stochastic framework has been studied.
📊
Key Findings & Data:It has been shown that like the unconstrained EKF, the estimation error of the EKF with known constraints on the states remains bounded when the initial error and noise terms are small, and the solution of the Riccati difference equation remains positive definite and bounded.
💡
Conclusion & Impact:Stability results are verified and performance of the constrained EKF is demonstrated through simulations on a nonlinear engineering example.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Extended Kalman filter · constrained Kalman filter · stochastic stability.

Abstract

In this paper, stability of the projection-based constrained discrete-time extended Kalman filter (EKF) as applied to nonlinear systems in a stochastic framework has been studied. It has been shown that like the unconstrained EKF, the estimation error of the EKF with known constraints on the states remains bounded when the initial error and noise terms are small, and the solution of the Riccati difference equation remains positive definite and bounded. Stability results are verified and performance of the constrained EKF is demonstrated through simulations on a nonlinear engineering example.

Özet

In this paper, stability of the projection-based constrained discrete-time extended Kalman filter (EKF) as applied to nonlinear systems in a stochastic framework has been studied. It has been shown that like the unconstrained EKF, the estimation error of the EKF with known constraints on the states remains bounded when the initial error and noise terms are small, and the solution of the Riccati difference equation remains positive definite and bounded. Stability results are verified and performance of the constrained EKF is demonstrated through simulations on a nonlinear engineering example.

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Cite This Article

ÖZBEK, L., BABACAN, E. K., EFE, M. (2010). Stochastic stability of the discrete-time constrained extended Kalman filter. Turkish Journal of Electrical Engineering and Computer Science, Cilt: 18 - Sayı: 2, 211–223. https://ajindex.com/articles/189177

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