Filter‐Based Secure Dynamic Pose Estimation
Xinghua Liu, Rui Jiang, Badong Chen, Shuzhi Sam Ge · 2022
Pose estimation is a critical problem in autonomous vehicle navigation, especially in circumstances involving sensor failures or attacks. In this chapter, a filter-based secure dynamic pose estimation approach is introduced such that the vehicle pose can be resilient under possible sensor attacks. Our proposed estimator coincides with the conventional Kalman filter when all sensors on autonomous vehicles are benign. If fewer than half of the measurement states consist of randomly occurring deception attacks, this chapter still gives stable estimates of the pose states: i.e. an upper bound for the estimation error covariance is guaranteed. Pose estimation results with single and multiple attacks on the testing route validate the effectiveness and robustness of the proposed approach.