Distributed Kalman Filter for 3-D Moving Object Tracking over Sensor Networks
Distributed Kalman Filter for 3-D Moving Object Tracking over Sensor Networks
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DOI：
10.1109/cdc42340.2020.9303740
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发表时间：
2020-12
期刊：
影响因子：
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通讯作者：
Pengxiang Zhu;W. Ren
中科院分区：
文献类型：
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作者：
Pengxiang Zhu;W. Ren
本文研究了传感器网络上的分布式状态估计（DSE）问题。与仅考虑在二维 (2-D) 环境中移动的目标的现有过滤算法不同，我们在三维 (3-D) 场景中解决这个问题，其中每个配备通信和传感功能的代理协作跟踪 3-D 移动对象的状态。首先，表明现有的分布式卡尔曼滤波器（DKF）算法无法解决基于四元数的六自由度（6DoF）运动跟踪。然后，针对一般非线性系统，引入了一种适用于 3D 跟踪的新型 DKF。所提出的算法是完全分布式的，并且对时变通信拓扑和变化的盲代理（看不见整个目标对象的代理）具有鲁棒性。最后，我们将所提出的算法应用于相机网络来跟踪移动目标对象的 6-DoF 位姿（位置和方向）。我们的方法的有效性通过蒙特卡罗模拟得到了证明。
This paper studies the problem of distributed state estimation (DSE) over sensor networks. Unlike the existing filtering algorithms that only consider targets moving in twodimension (2-D) environments, we address this problem in three-dimension (3-D) scenarios where each agent equipped with the communication and sensing capabilities cooperatively track the state of a 3-D moving object. First, it is shown that the existing distributed Kalman filter (DKF) algorithms cannot solve the quaternion-based six degree-of-freedom (6DoF) motion tracking. Then, a novel DKF applicable for the 3-D tracking is introduced for a general nonlinear system. The proposed algorithm is fully distributed and robust to timevarying communication topologies and changing blind agents (the agents that lose sight of the whole target object). Finally, we apply the proposed algorithm to a camera network to track the 6-DoF pose (position and orientation) of a moving target object. The effectiveness of our approach is demonstrated through Monte-Carlo simulations.