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Chapitre D'ouvrage Année : 2020

Guaranteed Nonlinear Parameter Estimation with Additive Gaussian Noise

Luc Jaulin

Résumé

In this paper we propose a new approach for nonlinear parameter estimation under additive Gaussian noise. We provide an algorithm based on interval analysis and set inversion which computes an inner and an outer approximation of a set enclosing the parameter vector with a given probability. The principle of the approach is illustrated by examples related to parameter estimation and range-only localization.
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Dates et versions

hal-02520046 , version 1 (03-05-2021)

Identifiants

Citer

Jérémy Nicola, Luc Jaulin. Guaranteed Nonlinear Parameter Estimation with Additive Gaussian Noise. Olga Kosheleva; Sergey P. Shary; Gang Xiang; Roman Zapatrin. Beyond Traditional Probabilistic Data Processing Techniques: Interval, Fuzzy etc. Methods and Their Applications, 835, Springer, pp.341-357, 2020, Studies in Computational Intelligence, 978-3-030-31041-7 (Ebook); 978-3-030-31040-0 (Hardcover). ⟨10.1007/978-3-030-31041-7_19⟩. ⟨hal-02520046⟩
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