具有传感器增益退化、随机时延和丢包的分布式融合估计器

研究了具有传感器增益退化、模型不确定性、数据传输时延和丢包的网络化多传感器分布式融合估计问题，模型的不确定性描述为系统矩阵受到随机扰动，传感器增益退化现象通过统计特性已知的随机变量来描述，随机时延和丢包现象存在于局部最优状态估计向融合中心传输的过程中.首先，设计了一种局部最优无偏估计器，然后将传输时延描述为随机过程，并在融合中心端建立符合存储规则的时延-丢包模型，利用最优线性无偏估计方法，导出最小方差意义下的分布式融合估计器.最后，通过算例仿真证明所设计融合估计器的有效性.

A Decentralized Fusion Estimator with Stochastic Sensor Gain Degradation,Delays and Data Dropouts

The decentralized fusion estimation problem is investigated in the paper for a class of networked uncertain stochastic systems with stochastic sensor gain degradation, data transmission delays, and data dropouts. The model's uncertainty is described by stochastic parameter perturbations considered in the system matrix. The sensor gain degradation is described by random variable whose probability is assumed to be known. Stochastic delays and data dropouts are considered in the process in which the locally optimal estimates are transmitted to the fusion node. Firstly, a locally optimal estimator is proposed. Then, the data transmission delays are considered to be a stochastic process, and a delay-dropout model is put forward to account for the storage mechanism. A decentralized fusion estimator is proposed by minimizing the mean square error on the best linear unbiased estimation (BLUE) criterion. Finally, a simulation example is given to confirm the effectiveness of the proposed approach.