对异步马尔科夫跳跃系统的事件触发分布式融合估计器,具有相关的噪声和色测量
1School of Electronic Engineering, Heilongjiang University, Harbin 150080, China.
Sensors (Basel, Switzerland)
|January 23, 2024
概括
本研究引入了一种事件触发的分布式聚变估计方法,用于与相关噪声和色测量相关的异步系统. 该方法通过模拟验证的基于差异的触发器来提高通信效率.
科学领域:
- 控制系统工程 控制系统工程
- 信号处理 信号处理
- 估计理论估计理论
背景情况:
- 异步系统在分布式估计方面存在挑战,原因是时间变化.
- 相对应的噪音和色测量使准确的状态估计变得复杂.
- 事件触发通信对于减少分布式系统中的网络负载至关重要.
研究的目的:
- 为异步马尔科夫跳跃系统开发事件触发的分布式聚变估计算法.
- 为了应对相关噪音和色测量所带来的挑战.
- 通过一种新的事件触发策略来提高沟通效率.
主要方法:
- 对异步系统的更新点进行状态同步.
- 为单个传感器开发局部过器.
- 在本地估计器和融合中心之间实施基于差异的事件触发策略.
- 关于使用矩阵加权聚变标准的分布式聚变估计算法的建议.
主要成果:
- 异步系统的成功同步.
- 在相关的噪音和色测量下进行有效的局部过.
- 通过事件触发策略显著减少网络通信的能源消耗.
- 通过计算机模拟验证拟议的分布式聚变估计算法.
结论:
- 建议的事件触发分布式聚变估计方法对于异步马尔科夫跳跃系统是有效的.
- 这种方法成功地处理了相关的噪音和色测量.
- 基于差异的事件触发策略提高了分布式估计网络的通信效率.
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