Adaptive Kalman Filtering Based on Variable Weight Innovation Covariance

As for poor robustness of traditional Kalman filtering and bad behavior of accurate tracking breaking state of the system, variable weight innovation covariance was designed to regulate adaptive Kalman filtering. About the algorithm, this paper first analyzed the reason of bad behavior of accurate tracking in the breaking state based on traditional Kalman filtering. By using criterion of filtering divergence, degree of state mutation was layered on the basis of the relationship between reserve coefficient and innovation covariance.Based on Sage-Husa estimation principle and weighted least squares method, according to different degree of state mutation, the technology of dynamically adjusting the weight of innovation covariance in the filter estimation was introduced. Fading factor was optimized.Filtering gain was activated in real-time. The weight of measurement innovation was enhanced. The case study result shows that the adaptive Kalman filter has strong robustness. It can get accurate tracking breaking state of the system, and the convergence rate is superior to the other rate of robust Kalman filtering, and the steady precision can be improved to 42.05 percent.