Robust guaranteed cost data fusion approach for uncertain systems

To deal with model uncertainties and disturbances in the multi-sensor data fusion system, a robust guaranteed cost data fusion approach is presented based on the theory of robust control and Kalman filtering. Both qualitative and quantitative data fusion issues are considered. It turns out that the robust filter can guarantee a bounded variance of filtering errors, when the system matrix and measure matrix exist uncertainties. Simulation results are given to show the validity of our methods.