Multi-sensor optimal information fusion steady-state Kalman filter weighted by scalars
Shuli Sun · Kongzhi yu juece · 2004
A new multi-sensor optimal information fusion criterion weighted by scalars is presented in the linear minimum variance sense. The criterion considers the correlation among local estimate errors, and only computing the weighted scalar coefficients is needed. Therefore the computational burden can obviously be reduced, and it is convenient to apply in real time. Using steady-state Kalman filtering theory, a multi-sensor optimal information fusion steady-state Kalman filter is given based on this fusion criterion. The information fusion steady-state filter can be obtained only by one time fusing after all local filters enter steady states. Simulation example shows the effectiveness of the proposed method.