Distributed Invariant Extended Kalman Filter for 3-D Dynamic State Estimation Using Lie Groups
Distributed Invariant Extended Kalman Filter for 3-D Dynamic State Estimation Using Lie Groups
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DOI：
10.23919/acc53348.2022.9867483
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发表时间：
2022-06
期刊：
影响因子：
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通讯作者：
J. Xu;Pengxiang Zhu;W. Ren
中科院分区：
文献类型：
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作者：
J. Xu;Pengxiang Zhu;W. Ren
分布式卡尔曼滤波器在矢量空间中得到了广泛的研究，并被应用于传感器网络的二维目标状态估计。在本文中，我们介绍了一种新的分布式不变扩展卡尔曼滤波器（DIEKF），利用矩阵李群，适用于跟踪目标的6-DOF运动在3-D环境。DIEKF是基于建议的扩展协方差交叉（CI）算法，保证矩阵李群的一致性。DIEKF是完全分布式的，因为每个代理只使用来自自身和一跳通信邻居的信息，并且它对时变通信拓扑和变化的盲代理具有鲁棒性。为了评估性能，我们应用该算法在摄像机网络跟踪目标姿态。广泛的蒙特-卡罗模拟已经进行了分析的性能。总体而言，该算法是更准确，更一致的比较，我们最近的工作基于四元数的分布式EKF（QDEKF）。
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).