# Convergence Rate of the Causal Jacobi Derivative Estimator

1 NON-A - Non-Asymptotic estimation for online systems
Inria Lille - Nord Europe, CRIStAL - Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189
5 SyNeR - Systèmes Non Linéaires et à Retards
CRIStAL - Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189
Abstract : Numerical causal derivative estimators from noisy data are essential for real time applications especially for control applications or fluid simulation so as to address the new paradigms in solid modeling and video compression. By using an analytical point of view due to Lanczos \cite{C. Lanczos} to this causal case, we revisit $n^{th}$\ order derivative estimators originally introduced within an algebraic framework by Mboup, Fliess and Join in \cite{num,num0}. Thanks to a given noise level $\delta$ and a well-suitable integration length window, we show that the derivative estimator error can be $\mathcal{O}(\delta ^{\frac{q+1}{n+1+q}})$ where $q$\ is the order of truncation of the Jacobi polynomial series expansion used. This so obtained bound helps us to choose the values of our parameter estimators. We show the efficiency of our method on some examples.
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Journal articles

Cited literature [22 references]

https://hal.inria.fr/inria-00599767
Contributor : Dayan Liu <>
Submitted on : Saturday, June 11, 2011 - 12:50:34 AM
Last modification on : Friday, March 22, 2019 - 1:33:59 AM
Long-term archiving on: Monday, September 12, 2011 - 2:20:24 AM

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### Citation

Da-Yan Liu, Olivier Gibaru, Wilfrid Perruquetti. Convergence Rate of the Causal Jacobi Derivative Estimator. Lecture Notes in Computer Science, Springer, 2011, Curves and Surfaces, 6920, ⟨10.1007/978-3-642-27413-8_28⟩. ⟨inria-00599767⟩

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