有丢包的随机不确定参数系统的最优融合滤波

Optimal fusion filtering for systems with stochastic parametric uncertainties and packet dropouts

研究了有数据丢包的带随机不确定参数的多传感器系统的分布式最优(线性最小方差)融合滤波问题。首先,引入虚拟噪声,将原系统转化为等价的参数确定的有丢包的新系统。然后,进行状态扩维,得到新系统的各子系统的扩维状态的滤波估计、滤波误差方差和滤波误差互协方差。根据扩维状态与原系统状态的关系,求出原系统状态的各局部滤波估计、滤波误差方差和滤波误差互协方差。利用线性最小方差意义下的矩阵加权最优融合算法,得到原系统的分布式矩阵加权最优融合滤波器。理论分析和仿真算例都表明,融合滤波器优于每一个局部滤波器。

The distributed optimal fusion problem for the state estimation of multi-sensor discrete-time systems with stochastic parametric uncertainties and packet dropouts was studied.By introducing fictitious noises,the original system was transformed into an equivalent system without uncertain parameters.For each subsystem of the equivalent system with packet dropouts,the local filtering estimate and the local filtering error covariance were obtained by using the innovation analysis method.After the filtering error cross-covariance matrices between local estimates were obtained,the distributed optimal（i.e.,linear minimum variance） fusion filters were developed by the fusion rule weighted by matrices.The simulation example showed that the fusion filter was better than each local filter.