Zhang Rui, Lin Honglei
School of Electronic Engineering, Heilongjiang University, Harbin 150080, China.
Sensors (Basel). 2024 Jan 5;24(2):0. doi: 10.3390/s24020336.
In this study, we investigate event-triggered distributed fusion estimation for asynchronous Markov jump systems subject to correlated noises and fading measurements. The measurement noises are interrelated, and they are simultaneously coupled with the system noise. The sensor samples measurements uniformly, and the sampling rates of the sensors are different. First, the asynchronous system is synchronized at state update points; then, the local filter is obtained. Furthermore, a variance-based event-triggered strategy is introduced between the local estimator and the fusion center to decrease the energy consumption of network communication. Then, a distributed fusion estimation algorithm is proposed using a matrix-weighted fusion criterion. Finally, the effectiveness of the algorithm is verified using computer simulations.
在本研究中，我们研究了受相关噪声和衰落测量影响的异步马尔可夫跳跃系统的事件触发分布式融合估计。测量噪声是相互关联的，并且它们与系统噪声同时耦合。传感器均匀地采样测量值，并且传感器的采样率不同。首先，在状态更新点对异步系统进行同步；然后，获得局部滤波器。此外，在局部估计器和融合中心之间引入了基于方差的事件触发策略，以降低网络通信的能量消耗。然后，使用矩阵加权融合准则提出了一种分布式融合估计算法。最后，通过计算机仿真验证了该算法的有效性。
