传感器带未知输入的离散随机线性系统的分布式融合滤波

Distributed Fusion Filter for Discrete-time Stochastic Linear Systems with Unknown Sensor Inputs

对传感器含未知输入和带相关噪声的离散随机线性系统,在没有未知输入的任何先验信息的情况下,设计了线性最小方差无偏状态滤波器。当系统带有多个传感器时,推得了任两个传感器子系统的滤波误差的互协方差阵。进而基于多传感器线性最小方差标量加权融合算法,给出了标量加权分布式融合状态滤波器。仿真研究验证了其有效性。

An unbiased state filter in the linear minimum variance sense is developed for discrete-time stochastic linear systems with unknown sensor inputs and correlated noises, where there is not any prior information for the unknown inputs. When there are multiple sensors, the cross-covariance matrix of filtering errors between any two sensors is derived. Further, the distributed scalar-weighted fusion state filter is given based on the multi-sensor optimal fusion algorithm in the linear minimum variance sense. A simulation example shows the effectiveness.