This chapter focuses on the problem of secure attitude estimation for autonomous vehicles. Based on the established attitude and heading reference system (AHRS) measuring model and the attack model, we have decomposed the optimal Kalman estimate into a linear combination of local state estimates. We then present a convex optimization‐based approach, instead of a 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 a certain probability when there is no attack and can be stable when elements of the model state are compromised. Simulations have been conducted to validate the proposed secure filter under single and multiple measurement attacks.
Secure Dynamic State Estimation for AHRS
2022-09-27
16 pages
Article/Chapter (Book)
Electronic Resource
English
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