Adaptive Random Weighted H∞ Estimation for System Noise Statistics
Zhaohui Gao,
Yongmin Zhong,
Hua Zong
et al.
Abstract: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∞ filtering‐based on adaptive random weighted estimation to address this issue. It combines the H∞… Show more
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