Kalman and UFIR state estimation with coloured measurement noise using backward Euler method Article Swipe

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Yuriy S. Shmaliy , Shunyi Zhao , Choon Ki Ahn ·

YOU? · · 2019 · Open Access · · DOI: https://doi.org/10.1049/iet-spr.2019.0166

Under coloured noise, known modifications of the Kalman filter (KF) exist only for discrete‐time state‐space models produced by the forward Euler (FE) method, which fits with feedback control. In this study, the authors modify the KF and unbiased finite impulse response (UFIR) filter using the backward Euler (BE) method for models with coloured measurement noise (CMN), which better fits systems without feedback. The FE‐ and BE‐based models differ by time indexes in the system input and noise that is essential for time‐varying and Markov jump systems. Employing measurement differencing, two KF algorithms and a unique UFIR algorithm are derived for time‐correlated and de‐correlated noise. An equivalence of the KF algorithms is proved analytically and confirmed by simulations. Numerical examples are given for target tracking and experimental verification is provided for visual object tracking. The high efficiency of the designed algorithms in removing CMN is demonstrated experimentally.

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Kalman filter Noise (video) Computer science Control theory (sociology) Algorithm Euler method State space Tracking (education) Euler's formula Impulse (physics) Mathematics Artificial intelligence Statistics Control (management) Mathematical analysis Quantum mechanics Psychology Pedagogy Physics Image (mathematics)

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Type: article
Language: en
Landing Page: https://doi.org/10.1049/iet-spr.2019.0166
PDF: https://ietresearch.onlinelibrary.wiley.com/doi/pdfdirect/10.1049/iet-spr.2019.0166
OA Status: bronze
Cited By: 45
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DOI: https://doi.org/10.1049/iet-spr.2019.0166

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Title: Kalman and UFIR state estimation with coloured measurement noise using backward Euler method

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Language: en

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Publication year: 2019

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Publication date: 2019-10-19

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Authors: Yuriy S. Shmaliy, Shunyi Zhao, Choon Ki Ahn

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Landing page: https://doi.org/10.1049/iet-spr.2019.0166

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Concepts: Kalman filter, Noise (video), Computer science, Control theory (sociology), Algorithm, Euler method, State space, Tracking (education), Euler's formula, Impulse (physics), Mathematics, Artificial intelligence, Statistics, Control (management), Mathematical analysis, Quantum mechanics, Psychology, Pedagogy, Physics, Image (mathematics)

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Cited by: 45

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