分布式合作卡尔曼波器受限于移动传感器网络的向导-扩散方程
Ziqiao Zhang1, Scott T Mayberry2, Wencen Wu3
1School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, United States.
Frontiers in robotics and AI
|June 23, 2023
概括
一个新的分布式合作过策略增强了移动传感器网络的状态估计. 这种方法改善了时空场分析,并使基于梯度的形成控制成为可能.
科学领域:
- 机器人和控制系统 机器人和控制系统
- 分布式系统 分布式系统
- 传感器网络 传感器网络
背景情况:
- 移动传感器网络需要在动态环境中准确的状态估计.
- 时空场,通常是通过向导-扩散方程建模的,为分布式传感带来了独特的挑战.
研究的目的:
- 开发一种分布式合作过策略,用于移动传感器网络中的状态估计.
- 为了使在时空领域运行的传感器网络能够基于梯度的形成控制.
主要方法:
- 用一个受约束的合作卡尔曼波器在分布式单元格的网格中进行状态估计.
- 使用有限体积法来分辨向-扩散方程,为卡尔曼波器建立约束.
- 基于梯度的形成控制定律是使用估计的场梯度信息来开发的.
主要成果:
- 分布式受约束的合作卡尔曼波器成功估计了场值和梯度.
- 基于梯度的形成控制法有效地调整了传感器网络的形成大小.
- 收分析证实了过和控制策略的稳定性.
结论:
- 建议的分布式过和控制方法已为移动传感器网络进行验证.
- 这种方法在复杂的时空领域提供了增强的状态估计和适应性形成能力.
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