论文标题
具有网络诱导的复杂性和多重噪声的随机不确定系统的分布式融合估计
Distributed Fusion Estimation for Stochastic Uncertain Systems with Network-Induced Complexity and Multiple Noise
论文作者
论文摘要
本文研究了网络诱导的复杂性和随机参数不确定性下的分布式融合估计问题。首先，开发了一种基于事件触发器的新型信号选择方法，以处理由随机传输延迟引起的网络诱导的数据包删除以及在不同噪声环境中分析系统性能的$ {H_2}/{H_2}/{H_2}/{H_2}/{H_2}/{H_2}/{H_2}/{H_2}/{H_2}/{H_2}/{H_2}/{H_2}/{H_2}/{H_2}/{H_2}/{H_2}/{H_2}/{此外，进一步采用了线性延迟补偿策略来解决复杂性网络引起的问题，这可能会恶化系统性能。此外，加权融合方案用于通过误差交叉协方差矩阵整合多个资源。一些案例研究验证了所提出的算法，并证明了目标跟踪中的系统性能令人满意。
This paper investigates an issue of distributed fusion estimation under network-induced complexity and stochastic parameter uncertainties. First, a novel signal selection method based on event-trigger is developed to handle network-induced packet dropouts as well as packet disorders resulting from random transmission delays, where the ${H_2}/{H_\infty }$ performance of the system is analyzed in different noise environments. In addition, a linear delay compensation strategy is further employed for solving the complexity network-induced problem, which may deteriorate the system performance. Moreover, a weighted fusion scheme is used to integrate multiple resources through an error cross-covariance matrix. Several case studies validate the proposed algorithm and demonstrate satisfactory system performance in target tracking.
