Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/213076
Title: A New Outlier-Robust Student's t Based Gaussian Approximate Filter for Cooperative Localization
Authors: Yulong Huang;Yonggang Zhang;Bo Xu;Zhemin Wu;Jonathon Chambers
Year: 2017
Publisher: IEEE
Abstract: In this paper, a new outlier-robust Student's t based Gaussian approximate filter is proposed to address the heavy-tailed process and measurement noises induced by the outlier measurements of velocity and range in cooperative localization of autonomous underwater vehicles (AUVs). The state vector, scale matrices, and degrees of freedom (DOF) parameters are jointly estimated based on the variational Bayesian approach by using the constructed Student's t based hierarchical Gaussian state-space model. The performances of the proposed filter and existing filters are tested in the cooperative localization of an AUV through a lake trial. Experimental results illustrate that the proposed filter has better localization accuracy and robustness than existing state-of-the-art outlier-robust filters.
URI: http://localhost/handle/Hannan/213076
volume: 22
issue: 5
More Information: 2380,
2386
Appears in Collections:2017

Files in This Item:
File SizeFormat 
8016598.pdf768.11 kBAdobe PDF
Title: A New Outlier-Robust Student's t Based Gaussian Approximate Filter for Cooperative Localization
Authors: Yulong Huang;Yonggang Zhang;Bo Xu;Zhemin Wu;Jonathon Chambers
Year: 2017
Publisher: IEEE
Abstract: In this paper, a new outlier-robust Student's t based Gaussian approximate filter is proposed to address the heavy-tailed process and measurement noises induced by the outlier measurements of velocity and range in cooperative localization of autonomous underwater vehicles (AUVs). The state vector, scale matrices, and degrees of freedom (DOF) parameters are jointly estimated based on the variational Bayesian approach by using the constructed Student's t based hierarchical Gaussian state-space model. The performances of the proposed filter and existing filters are tested in the cooperative localization of an AUV through a lake trial. Experimental results illustrate that the proposed filter has better localization accuracy and robustness than existing state-of-the-art outlier-robust filters.
URI: http://localhost/handle/Hannan/213076
volume: 22
issue: 5
More Information: 2380,
2386
Appears in Collections:2017

Files in This Item:
File SizeFormat 
8016598.pdf768.11 kBAdobe PDF
Title: A New Outlier-Robust Student's t Based Gaussian Approximate Filter for Cooperative Localization
Authors: Yulong Huang;Yonggang Zhang;Bo Xu;Zhemin Wu;Jonathon Chambers
Year: 2017
Publisher: IEEE
Abstract: In this paper, a new outlier-robust Student's t based Gaussian approximate filter is proposed to address the heavy-tailed process and measurement noises induced by the outlier measurements of velocity and range in cooperative localization of autonomous underwater vehicles (AUVs). The state vector, scale matrices, and degrees of freedom (DOF) parameters are jointly estimated based on the variational Bayesian approach by using the constructed Student's t based hierarchical Gaussian state-space model. The performances of the proposed filter and existing filters are tested in the cooperative localization of an AUV through a lake trial. Experimental results illustrate that the proposed filter has better localization accuracy and robustness than existing state-of-the-art outlier-robust filters.
URI: http://localhost/handle/Hannan/213076
volume: 22
issue: 5
More Information: 2380,
2386
Appears in Collections:2017

Files in This Item:
File SizeFormat 
8016598.pdf768.11 kBAdobe PDF