两传感器广义系统信息融合Kalman滤波器

Two Sensors Information Fusion Kalman Filter for Descriptor Systems

基于矩阵理论和时域上的Kalman滤波理论,针对两传感器广义离散随机线性系统进行研究,运用矩阵约当分解,将一类广义系统化为正常系统,并利用标量加权准则下的最优信息融合Kalman滤波理论,给出一类两传感器广义系统Kalman信息融合滤波器,同单传感器情形相比,可大大提高滤波精度,具有实际应用价值。

Based on matrix theory and Kalman filtering theory in the time domain, two sensors linear discrete-time descriptor stochastic systems are studied. Using Jordan decomposition for matrixes, descriptor systems can he changed into normal systems. An information fusion Kalman filter is given for two sensors descriptor systems by using optimal information fusion Kalman filtering weighted by scalars. Comparing with the single sensor case, the accuracy of filter is improved. It is convenient to apply in real time.