使用带有量化测量的传感器网络对场的分布式估计
Chethaka Jayasekaramudeli1, Alex S Leong2, Alexei T Skvortsov2
1Faculty of Engineering and Information Technology, University of Melbourne, Parkville 3010, Australia.
Sensors (Basel, Switzerland)
|August 29, 2024
概括
本研究介绍了两个分布式算法,测量扩散和估计扩散,用于使用带有量化数据的传感器网络来估计标量场. 这两种方法都实现了接近集中估计的性能,即使对于时间变化的领域.
科学领域:
- 传感器网络 传感器网络
- 分布式估计 分布式估计
- 信号处理 信号处理
背景情况:
- 对于环境监测等应用来说,尺度场估计至关重要.
- 现有的方法通常需要集中处理,这对于大型传感器网络来说可能是低效的.
- 量子化传感器测量在分布式估计中带来了挑战.
研究的目的:
- 开发和评估使用带有量化测量的传感器网络用于标量场的分布式估计算法.
- 提出两个新的方案:测量扩散和估计扩散.
- 评估这些算法的性能,包括它们处理时间变化的字段的能力.
主要方法:
- 分布式估计算法,传感器在本地与邻居共享信息.
- 测量扩散:传感器广播收到的测量.
- 估计扩散:传感器播放当地估计和Hessians.
- 每个传感器上的信息的代组合.
主要成果:
- 测量扩散和估计扩散方案都有效地估计了标量场.
- 算法可以处理时间变化的标量场.
- 数字研究表明稳定状态的性能与集中估计相比较.
结论:
- 使用量子化传感器数据进行分布式估计是可行的,使用拟议的扩散方案.
- 这些方法为集中估计提供了可行的替代方案,特别是在大型传感器网络中.
- 这些算法在估计标量场时表现出了强度和效率.
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