Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/601375
Title: Robust student’s t based nonlinear filter and smoother
Authors: Yulong Huang;Yonggang Zhang;Ning Li;Jonathon Chambers
subject: Smoothing methods|Target tracking|Probability density function|Covariance matrices|Nonlinear systems|Noise measurement|Handheld computers
Year: 2016
Publisher: IEEE
Abstract: Novel Student’s t based approaches for formulating a filter and smoother, which utilize heavy tailed process and measurement noise models, are found through approximations of the associated posterior probability density functions. Simulation results for manoeuvring target tracking illustrate that the proposed methods substantially outperform existing methods in terms of the root mean square error.
Description: 
URI: http://localhost/handle/Hannan/155218
http://localhost/handle/Hannan/601375
ISSN: 0018-9251
volume: 52
issue: 5
Appears in Collections:2016

Files in This Item:
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Title: Robust student’s t based nonlinear filter and smoother
Authors: Yulong Huang;Yonggang Zhang;Ning Li;Jonathon Chambers
subject: Smoothing methods|Target tracking|Probability density function|Covariance matrices|Nonlinear systems|Noise measurement|Handheld computers
Year: 2016
Publisher: IEEE
Abstract: Novel Student’s t based approaches for formulating a filter and smoother, which utilize heavy tailed process and measurement noise models, are found through approximations of the associated posterior probability density functions. Simulation results for manoeuvring target tracking illustrate that the proposed methods substantially outperform existing methods in terms of the root mean square error.
Description: 
URI: http://localhost/handle/Hannan/155218
http://localhost/handle/Hannan/601375
ISSN: 0018-9251
volume: 52
issue: 5
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7812899.pdf497.5 kBAdobe PDFThumbnail
Preview File
Title: Robust student’s t based nonlinear filter and smoother
Authors: Yulong Huang;Yonggang Zhang;Ning Li;Jonathon Chambers
subject: Smoothing methods|Target tracking|Probability density function|Covariance matrices|Nonlinear systems|Noise measurement|Handheld computers
Year: 2016
Publisher: IEEE
Abstract: Novel Student’s t based approaches for formulating a filter and smoother, which utilize heavy tailed process and measurement noise models, are found through approximations of the associated posterior probability density functions. Simulation results for manoeuvring target tracking illustrate that the proposed methods substantially outperform existing methods in terms of the root mean square error.
Description: 
URI: http://localhost/handle/Hannan/155218
http://localhost/handle/Hannan/601375
ISSN: 0018-9251
volume: 52
issue: 5
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7812899.pdf497.5 kBAdobe PDFThumbnail
Preview File