多传感器时滞系统数据丢失时滤波融合算法

Fusion of Multi-sensor Time-delay Systems with Random Data Loss

实际的工程项目中经常涉及到多传感器时滞系统,数据在传输中不仅存在着过程、测量噪声的干扰,还出现了丢失现象。为了获得准确的状态信息,需要研究测量数据发生随机丢失的多传感器时滞的信息融合问题。基于矩阵加权线性最小方差融合算法,对存在数据随机丢失的多传感器线性定常离散时滞系统,给出了一种增广分布式最优信息融合卡尔曼滤波器,并推导了任意2个传感器子系统之间的滤波误差互协方差阵计算公式。最后结合恒温控制系统实例,以温控中心的数据融合为背景,同时基于多传感器实时数据融合系统,分别对单传感器和双传感器情况进行仿真实验。仿真结果表明,分布式融合估计具有较高的精度,且易于故障检测和分离。

When data packets transmit in the multi-sensor network,the observation measurements with various noises and data loss are gotten in a random fashion.In order to get the exact state information,it is necessary to study the information fusion of multi-sensor time-delay system with random data loss.Based on optimal information fusion criterion weighted by matrix,an augmentation distributed weighted fusion optimal Kalman filter is proposed for linear time-invariant delayed systems with random data loss.The cross-covariance matrix of fdtering errors between any two-sensor subsystems is derived for time-delay system.Distributed fusion estimator could improve accuracy and easy for fault detection and separation.Its effectiveness can be showed by a simultion example.