Please use this identifier to cite or link to this item: http://localhost:80/handle/Hannan/154962
Title: Robust H-infinity CKF/KF hybrid filtering method for SINS alignment
Authors: Lei Zhang;Chun Yang;Qingwei Chen;Fei Yan
subject: misalignment angle|robust H-infinity CKF-KF hybrid filtering method|cubature Kalman filter|RHCHF|SINS alignment|strapdown inertial navigation system|in-motion alignment|unscented Kalman filter|statistical property|decomposition|nonlinear filtering method|nonlinear estimation
Year: 2016
Publisher: IEEE
Abstract: This study concerns the in-motion alignment in the strapdown inertial navigation system (SINS) with large misalignment angles. As the non-linear filtering method applied in the alignment model is quite computer intensive, which has a significant impact on the alignment accuracy and speed. To solve this problem, a robust H-infinity cubature Kalman filter (CKF)/KF hybrid filter (RHCHF) is proposed to lower the computational burden and strengthen the robustness. By virtue of the idea of model decomposition, the RHCHF could estimate the non-linear and linear parts of alignment model, respectively. Through the introduction of robust factor to adjust the filter parameters, it can ensure the accuracy reliably. The comparisons of the simulation and vehicle experiment demonstrate that the RHCHF could achieve the results at a significantly lower expense than the unscented Kalman filter, and obtain a high accuracy even when the statistical property of noise is uncertain or the outliers of measurement occur occasionally.
URI: http://localhost/handle/Hannan/154962
ISSN: 1751-8822
1751-8830
volume: 10
issue: 8
More Information: 916
925
Appears in Collections:2016

Files in This Item:
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Title: Robust H-infinity CKF/KF hybrid filtering method for SINS alignment
Authors: Lei Zhang;Chun Yang;Qingwei Chen;Fei Yan
subject: misalignment angle|robust H-infinity CKF-KF hybrid filtering method|cubature Kalman filter|RHCHF|SINS alignment|strapdown inertial navigation system|in-motion alignment|unscented Kalman filter|statistical property|decomposition|nonlinear filtering method|nonlinear estimation
Year: 2016
Publisher: IEEE
Abstract: This study concerns the in-motion alignment in the strapdown inertial navigation system (SINS) with large misalignment angles. As the non-linear filtering method applied in the alignment model is quite computer intensive, which has a significant impact on the alignment accuracy and speed. To solve this problem, a robust H-infinity cubature Kalman filter (CKF)/KF hybrid filter (RHCHF) is proposed to lower the computational burden and strengthen the robustness. By virtue of the idea of model decomposition, the RHCHF could estimate the non-linear and linear parts of alignment model, respectively. Through the introduction of robust factor to adjust the filter parameters, it can ensure the accuracy reliably. The comparisons of the simulation and vehicle experiment demonstrate that the RHCHF could achieve the results at a significantly lower expense than the unscented Kalman filter, and obtain a high accuracy even when the statistical property of noise is uncertain or the outliers of measurement occur occasionally.
URI: http://localhost/handle/Hannan/154962
ISSN: 1751-8822
1751-8830
volume: 10
issue: 8
More Information: 916
925
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7600489.pdf784.06 kBAdobe PDFThumbnail
Preview File
Title: Robust H-infinity CKF/KF hybrid filtering method for SINS alignment
Authors: Lei Zhang;Chun Yang;Qingwei Chen;Fei Yan
subject: misalignment angle|robust H-infinity CKF-KF hybrid filtering method|cubature Kalman filter|RHCHF|SINS alignment|strapdown inertial navigation system|in-motion alignment|unscented Kalman filter|statistical property|decomposition|nonlinear filtering method|nonlinear estimation
Year: 2016
Publisher: IEEE
Abstract: This study concerns the in-motion alignment in the strapdown inertial navigation system (SINS) with large misalignment angles. As the non-linear filtering method applied in the alignment model is quite computer intensive, which has a significant impact on the alignment accuracy and speed. To solve this problem, a robust H-infinity cubature Kalman filter (CKF)/KF hybrid filter (RHCHF) is proposed to lower the computational burden and strengthen the robustness. By virtue of the idea of model decomposition, the RHCHF could estimate the non-linear and linear parts of alignment model, respectively. Through the introduction of robust factor to adjust the filter parameters, it can ensure the accuracy reliably. The comparisons of the simulation and vehicle experiment demonstrate that the RHCHF could achieve the results at a significantly lower expense than the unscented Kalman filter, and obtain a high accuracy even when the statistical property of noise is uncertain or the outliers of measurement occur occasionally.
URI: http://localhost/handle/Hannan/154962
ISSN: 1751-8822
1751-8830
volume: 10
issue: 8
More Information: 916
925
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7600489.pdf784.06 kBAdobe PDFThumbnail
Preview File