Analysis of adaptive Kalman filter based on intelligent informationfusion techniques in integrated navigation system
Hongwei Bian, Jin Zhi-hua · Systems engineering and electronics · 2004
The adaptive Kalman filtering (AKF) based on intelligent information fusion algorithm has currently became an effective approach to enhance the integrated navigation system's robustness and accuracy. Three main intelligent adaptive algorithms, i.e. fuzzy inference reasoning based AKF (FIR-AKF), neural network based AKF (NN-AKF) and adaptive neural network-fuzzy inference system based AKF (ANFIS-AKF), are chosen specially and studied. The design rules of fuzzy controller, the key problem in FIR-AKF, are in detail analyzed respectively based on two aspects, i.e., the innovation sequence of Kalman filter and the working states of external reference system. The NN-AKF approaches can be used to settle different limitations of traditional Kalman filtering in model modification, fault diagnoses and fault isolation, and their valid applications are provided. Using ANFIS-AKF to generate the fuzzy inference rules in an adaptive INS is the final concern of this paper, and the basic design procedures are given.