The Adaptive Information Fusion Filtering Algorithm for Multi-constellation Integrated Navigation System
Chen Chuan-de · Aerospace Control · 2010
Regarding the multi-constellation integrated navigation,a double-adaptive federated filtering algorithm is proposed in this paper.By Assuming a current statistical model for maneuvering target,a dynamic positioning Kalman filtering model is established,which is based on adaptive acceleration and variance of maneuvering target,and adaptive sub-filters for GPS,GLONASS and GALILEO system are designed respectively.Then data fusion and processing for sub-filters are implemented by means of adaptive federated filtering algorithm.The information distribution coefficients of sub-filters are adaptively adjusted according to the geometry dilution of precision(GDOP),which are produced in real time by each satellite-navigation system.The double-adaptive filtering algorithm is applied on GPS/GLONASS/GALILEO multi-constellation integrated navigation system.The simulation results compared with weighted average filter and general federated filter show that the double-adaptive filter algorithm effectively improves the precision and reliability of integrated navigation.The algorithm proposed is more applicable for the satellite integrated navigation system in which measurement noise is time-varying.