具有传感器增益退化的不确定系统融合估计器

Fusion estimator with stochastic sensor gain degradation for uncertain systems

研究具有传感器增益退化、模型不确定性的多传感器融合估计问题,其中传感器增益退化现象描述为统计特性已知的随机变量,模型的不确定性描述为系统矩阵受到随机扰动.设计一种局部无偏估计器结构,并建立以局部估计器增益为决策变量、以有限时域下融合估计误差为代价函数的优化问题.在给出标量融合权重时,考虑到求得最优的局部估计器增益的解析形式较为困难,通过最小化代价函数的上界得到一组次优的局部估计器增益.最后通过算例仿真表明了所设计融合估计器的有效性.

The fusion estimation problem is investigated for a class of uncertain stochastic systems with stochastic sensor gain degradation. The sensor gain degradation is described by random variable whose probability is assumed to be known.The model＇s uncertainty is described by stochastic parameter perturbations considered in the system matrix. A kind of local unbiased estimator structure is proposed, and an optimization problem which sets the local filter gains and the finite horizon estimation error to be the decision variables and the cost function, respectively, is established. Then for the given scalar fusion weights, 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 bound of the cost function. Finally, simulation example is given to illustrate the effectiveness of the proposed approach.