Approximate Gaussian conjugacy: parametric recursive filtering under nonlinearity, multimodality, uncertainty, and constraint, and beyond-Reference-Cited by-同舟云学术

Approximate Gaussian conjugacy: parametric recursive filtering under nonlinearity, multimodality, uncertainty, and constraint, and beyond

Published:2017-12 Issue:12 Volume:18 Page:1913-1939
ISSN:2095-9184
Container-title:Frontiers of Information Technology & Electronic Engineering
language:en
Short-container-title:Frontiers Inf Technol Electronic Eng

Author:

Li Tian-cheng^ORCID,Su Jin-ya,Liu Wei,Corchado Juan M.

Funder

the Marie Skłodowska-Curie Individual Fellowship (H2020-MSCA-IF-2015)

the Open Project Program of Ministry of Education Key Laboratory of Measurement and Control of Complex Systems of Engineering, Southeast University, China

Publisher

Zhejiang University Press

Subject

Electrical and Electronic Engineering,Computer Networks and Communications,Hardware and Architecture,Signal Processing

Link

http://link.springer.com/article/10.1631/FITEE.1700379/fulltext.html

Reference233 articles.

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2. Afshari, H., Gadsden, S., Habibi, S., 2017. Gaussian filters for parameter and state estimation: a general review of theory and recent trends. Signal Process., 135:218–238. https://doi.org/10.1016/j.sigpro.2017.01.001

3. Agamennoni, G., Nebot, E.M., 2014. Robust estimation in non-linear state-space models with state-dependent noise. IEEE Trans. Signal Process., 62(8):2165–2175. https://doi.org/10.1109/TSP.2014.2305636

4. Ali-Loytty, S.S., 2010. Box Gaussian mixture filter. IEEE Trans. Autom. Contr., 55(9):2165–2169. https://doi.org/10.1109/TAC.2010.2051486

5. Arasaratnam, I., Haykin, S., 2008. Square-root quadrature Kalman filtering. IEEE Trans. Signal Process., 56(6):2589–2593. https://doi.org/10.1109/TSP.2007.914964

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