Adaptive Random Weighted H∞ Estimation for System Noise Statistics

The Kalman filter is an important technique for system state estimation. It requires the exact knowledge of system noise statistics to achieve optimal state estimation. However, in practice, this knowledge is often unknown or inaccurate due to uncertainties and disturbances involved in the dynamic environment, leading to degraded or even divergent Kalman filtering solutions. This paper proposes a novel method of H infinity filtering-based on adaptive random weighted estimation to address this issue. It combines the H infinity filter with random weighted concept to estimate system noise statistics. Random weighting theories are established based on the state estimate and state error covariance of the H infinity filter to estimate both process noise statistics and measurement noise statistics. Subsequently, the estimated system noise statistics are fed back into the Kalman filtering process for system state estimation. Simulation and experimental results show that the proposed method can effectively estimate system noise statistics, leading to improved accuracy for system state estimation.