Skip to Main content Skip to Navigation
Book sections

Guaranteed Nonlinear Parameter Estimation with Additive Gaussian Noise

Jérémy Nicola 1 Luc Jaulin 1
1 Lab-STICC_ENSTAB_CID_PRASYS
Lab-STICC - Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance
Abstract : 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.
Complete list of metadata

https://hal.archives-ouvertes.fr/hal-02520046
Contributor : Marie Briec <>
Submitted on : Monday, May 3, 2021 - 3:46:30 PM
Last modification on : Tuesday, May 4, 2021 - 1:10:15 PM

File

paper_region.pdf
Files produced by the author(s)

Identifiers

Citation

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⟩

Share

Metrics

Record views

310

Files downloads

15