在动态通信重量下的多传感器网络的最佳分布式有限时间融合方法
Hang Yu1, Keren Dai1, Qingyu Li2
1School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China.
Sensors (Basel, Switzerland)
|September 9, 2023
概括
本研究介绍了传感器网络的最佳分布式有限时间聚变过方法. 这种新的方法通过使用动态通信权重和快速有限时间融合来最大限度地减少融合错误来提高状态估计的准确性.
科学领域:
- 分布式系统 分布式系统
- 传感器网络 传感器网络
- 估计理论估计理论
背景情况:
- 在传感器网络中分布式状态估计面临着由于节点信息不完整而导致的融合错误的挑战.
- 现有的方法可能在动态网络环境中难以融合和准确.
研究的目的:
- 为传感器网络开发一种新的最佳分布式有限时间聚变过方法.
- 用动态通信权重来解决融合错误并提高状态估计的准确性.
- 为了实现全球信息聚合的快速有限时间融合.
主要方法:
- 在有限的代中构建了局部过算法架构,用于在有限的代中实现融合错误的融合.
- 根据通信拓图形直径,确定最大的代数.
- 采用矩阵重量聚变来优化局部选结果的估计 (最小方差).
- 引入了通用信息质量 (GIQ) 来得出相对通信权重,并入了融合算法.
主要成果:
- 拟议的方法在有限数量的代中实现了融合错误的融合.
- 通过矩阵重量融合,可以获得最小方差的最佳估计.
- 基于GIQ和局部偏差的动态通信权重提高了聚变的准确性.
- 数字模拟和实验测试验证了算法的有效性和可行性.
结论:
- 开发的最佳分布式有限时间聚变过方法有效地减少了传感器网络中的错误.
- 动态通信权重显著提高了分布式状态估计的准确性.
- 该算法展示了快速的有限时间融合和实际应用.
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