Auto Regressive Moving Average (ARMA) Modeling Method for Gyro Random Noise Using a Robust Kalman Filter

To solve the problem in which the conventional ARMA modeling methods for gyro random noise require a large number of samples and converge slowly, an ARMA modeling method using a robust Kalman filtering is developed. The ARMA model parameters are employed as state arguments. Unknown time-varying esti...

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Detalles Bibliográficos
Autor principal: Huang, Lei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2015
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4634477/
https://www.ncbi.nlm.nih.gov/pubmed/26437409
http://dx.doi.org/10.3390/s151025277

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4634477/
https://www.ncbi.nlm.nih.gov/pubmed/26437409
http://dx.doi.org/10.3390/s151025277

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