Hierarchical Adaptive Information Filtering Algorithm Considering System Noise and Observation Noise

Abstract

High-precision carrier dynamic navigation and positioning requires not only good control of abnormal disturbance and observation abnormality, but also accurate recognition and processing of time-varying characteristics of noise and observation noise in equation of state system. Aiming at the system noise dynamics model and the time-varying observation noise navigation system, and based on information filtering, a hierarchical adaptive filtering algorithm is proposed. The unbiasedness and effectiveness of the new algorithm are proved. Considering the gradual and fast changes of system noise, the new algorithm adds forgetting factor or two-stage adaptive factor to improve the stability of noise estimation for catastrophic systems. In addition, considering the time-varying of the observation noise, two different difference observation data are used and the covariance of the observation noise is effectively estimated by using the high-precision equation of state. The simulation and experimental results show that the new filtering algorithm can not only estimate the system noise simply and effectively, but also estimate the covariance matrix of the observation noise effectively, which improves the accuracy of parameter estimation of dynamic system.