Optimal Fusion Distributed Filter for Systems with Unknown Constant Sensor Bias

Based on the multi-sensor optimal fusion algorithm weighted by matrices in the linear minimum variance sense, a steady-state optimal fusion distributed filter is given for discrete-time linear stochastic control systems with multiple sensors having unknown constant sensor bias, which involves the fusion estimation of common state components for a system with multiple models and multiple sensors by state augmentation. The steady-state optimal fusion distributed filter has the reduced online computational burden. Also the distributed filter has better reliability. Applying it into a tracking system with three sensors shows its effectiveness.