具有传感器增益退化、传输时延和丢包的离线状态估计器

研究了具有传感器增益退化、数据传输时延和丢包的网络化状态估计问题,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,数据包时延和丢失发生于传感器量测输出向远程处理中心传送过程中,将各时延的发生描述为随机过程,在远程处理中心端建立只存储最新时刻数据包的时延-丢包模型,考虑到利用每一时刻实时的时延值和丢包情况,设计了一种离线的无偏估计器,推导出最小方差原则下的离线最优估计器增益.最后,通过算例仿真验证所设计离线状态估计器的有效性.

An Ofi-line State Estimator With Sensor Gain Degradation,Transmission Delays and Data Dropouts

The state estimation problem is investigated for a class of networked stochastic systems subject to sensor gain degradation, stochastic data transmission delays, and data dropouts. The sensor gain degradation is described by random variable whose probability is assumed to be known. Stochastic delays and data dropouts are considered when the sensor transmits its measurement to the remote processing node. Data transmission delays are considered to be a stochastic process, and a delay-dropout model is put forward to account for receiving the newest packet. An off-line unbiased state estimator is proposed to make good use of real-time delay and dropout information, and the optimal estimator gain is obtained by minimizing the mean square error. Finally, a simulation example is given to confirm the effectiveness of the proposed approach.