Distributed Invariant Extended Kalman Filter for 3-D Dynamic State Estimation Using Lie Groups
Jie Xu, Pengxiang Zhu, Wei Ren · 2022 American Control Conference (ACC) · 2022
Distributed Kalman filters have been widely studied in vector space and been applied to 2-D target state estimation using sensor networks. In this paper, we introduce a novel distributed invariant extended Kalman filer (DIEKF) that exploits matrix Lie groups and is suitable to track the target’s 6-DOF motion in a 3-D environment. The DIEKF is based on the proposed extended Covariance Intersection (CI) algorithm that guarantees consistency in matrix Lie groups. The DIEKF is fully distributed as each agent only uses the information from itself and the one-hop communication neighbors, and it is robust to a time-varying communication topology and changing blind agents. To evaluate the performance, we apply the algorithm in a camera network to track a target pose. Extensive Monte-Carlo simulations have been performed to analyze the performance. Overall, the proposed algorithm is more accurate and more consistent in comparison with our recent work on the quaternion-based distributed EKF (QDEKF).