Maximum Likelihood-Based Iterated Divided Difference Filter for Nonlinear Systems from Discrete Noisy Measurements

A new filter named the maximum likelihood-based iterated divided difference filter (MLIDDF) is developed to improve the low state estimation accuracy of nonlinear state estimation due to large initial estimation errors and nonlinearity of measurement equations. The MLIDDF algorithm is derivative-fre...

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Detalles Bibliográficos
Autores principales: Wang, Changyuan, Zhang, Jing, Mu, Jing
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Molecular Diversity Preservation International (MDPI) 2012
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3444083/
https://www.ncbi.nlm.nih.gov/pubmed/23012525
http://dx.doi.org/10.3390/s120708912

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3444083/
https://www.ncbi.nlm.nih.gov/pubmed/23012525
http://dx.doi.org/10.3390/s120708912

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