具有随机传感器测量衰减的多传感器融合估计器

研究了传感器存在测量输出随机衰减的不确定系统的融合状态估计问题。首先将系统中的不确定性用系统矩阵中的随机乘性干扰表示,测量衰减利用统计特性已知的随机变量进行描述;然后设计了一种局部状态估计结构,并以所有局部估计器的增益组成为决策变量,建立以对角加权估计误差平方和为目标函数的数学优化问题;由于最小化代价函数得到解析形式的决策变量是极其困难的,通过选取代价函数的一个上界并对其最小化,得到次优的决策增益;最后,利用算例仿真来验证有效性。

Multi-sensor fusion estimate approach for stochastic fading sensor measurement

The multi-sensor fusion estimation problem is investigated in the paper for a class of uncertain systems with stochastic sensor fading measurement. Firstly,the model’s uncertainty is described by multiplicative noise considered in the system matrix. The sensor gain degradation is described by random variable that statistic characteristics are assumed to be known. A kind of local estimator is proposed,and an optimization problem which sets the local filter gains and the diagonal weighted fusion estimation error to be decision variables and cost function,respectively,is established. As obtaining the closed form of the optimal consensus filter gains is a challenging problem,so a set of sub-optimal local filter gains are computed based on minimizing an upper a bound of the cost function.Finally,simulation example is given to confirm the effectiveness of the proposed approach.