Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/716993
Title: Secure Estimation for Attitude and Heading Reference Systems Under Sparse Attacks
Other Titles: IEEE Sensors Journal
Authors: Rui Jiang|Xinghua Liu|Han Wang|Shuzhi Sam Ge
subject: attitude and heading reference system (AHRS)|Secure attitude estimation|Kalman filter
Year: -1-Uns- -1
Abstract: This paper focuses on the problem of secure attitude estimation for autonomous vehicles. Based on the established AHRS measuring model and the attack model, we have decomposed the optimal Kalman estimate into a linear combination of local state estimates. We then propose a convex optimization-based approach, instead of the weighted sum approach, to combine the local estimate into a more secure estimate. It is shown that the proposed secure estimator coincides with the Kalman estimator with certain probability when there is no attack, and can be stable when p elements of the model state are compromised. Simulations have been conducted to validate the proposed secure filter under single and multiple measurement attacks.
URI: http://localhost/handle/Hannan/716993
ISBN: 1530-437X
volume: Volume
issue: Issue
Appears in Collections:New Ieee 2019

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Title: Secure Estimation for Attitude and Heading Reference Systems Under Sparse Attacks
Other Titles: IEEE Sensors Journal
Authors: Rui Jiang|Xinghua Liu|Han Wang|Shuzhi Sam Ge
subject: attitude and heading reference system (AHRS)|Secure attitude estimation|Kalman filter
Year: -1-Uns- -1
Abstract: This paper focuses on the problem of secure attitude estimation for autonomous vehicles. Based on the established AHRS measuring model and the attack model, we have decomposed the optimal Kalman estimate into a linear combination of local state estimates. We then propose a convex optimization-based approach, instead of the weighted sum approach, to combine the local estimate into a more secure estimate. It is shown that the proposed secure estimator coincides with the Kalman estimator with certain probability when there is no attack, and can be stable when p elements of the model state are compromised. Simulations have been conducted to validate the proposed secure filter under single and multiple measurement attacks.
URI: http://localhost/handle/Hannan/716993
ISBN: 1530-437X
volume: Volume
issue: Issue
Appears in Collections:New Ieee 2019

Files in This Item:
File Description SizeFormat 
08502820.pdf1.75 MBAdobe PDFThumbnail
Preview File
Title: Secure Estimation for Attitude and Heading Reference Systems Under Sparse Attacks
Other Titles: IEEE Sensors Journal
Authors: Rui Jiang|Xinghua Liu|Han Wang|Shuzhi Sam Ge
subject: attitude and heading reference system (AHRS)|Secure attitude estimation|Kalman filter
Year: -1-Uns- -1
Abstract: This paper focuses on the problem of secure attitude estimation for autonomous vehicles. Based on the established AHRS measuring model and the attack model, we have decomposed the optimal Kalman estimate into a linear combination of local state estimates. We then propose a convex optimization-based approach, instead of the weighted sum approach, to combine the local estimate into a more secure estimate. It is shown that the proposed secure estimator coincides with the Kalman estimator with certain probability when there is no attack, and can be stable when p elements of the model state are compromised. Simulations have been conducted to validate the proposed secure filter under single and multiple measurement attacks.
URI: http://localhost/handle/Hannan/716993
ISBN: 1530-437X
volume: Volume
issue: Issue
Appears in Collections:New Ieee 2019

Files in This Item:
File Description SizeFormat 
08502820.pdf1.75 MBAdobe PDFThumbnail
Preview File