基于反结构的最佳线性波器,用于与相关噪声和数据包掉落的传感网络
Weichen Shang1, Hang Yu1, Qingyu Li2
1School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210018, China.
Sensors (Basel, Switzerland)
|July 8, 2023
概括
本研究引入了基于反的矩阵重量融合方法,以解决分布式传感网络中的相关噪声和数据包丢失问题,提高信息融合的准确性.
科学领域:
- 信号处理 信号处理
- 网络化系统 网络化系统
- 数据融合数据融合
背景情况:
- 分布式传感网络在信息融合过程中面临相关噪音和数据包丢失的挑战.
- 准确的数据融合对于可靠的网络性能和决策至关重要.
研究的目的:
- 开发用于估计相关噪声和减轻分布式传感网络中的数据包丢失的方法.
- 提高网络系统中信息融合的准确性和可靠性.
主要方法:
- 建议使用带有反结构的矩阵重量融合方法来管理多传感器测量和估计噪声之间的相互关系.
- 使用具有反结构的预测器来补偿多传感器信息融合期间的数据包丢失.
主要成果:
- 拟议的矩阵重量融合方法在线性最小方程意义上实现了最佳估计.
- 基于反的预测器有效地降低了受数据包丢失影响的融合结果的协差.
结论:
- 开发的算法成功地解决了传感器网络信息融合中的相关噪声和数据包丢失问题.
- 反机制显著降低了聚变共变性,提高了整体系统性能.
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