An Adaptive Federated Filter Based on Variational Bayes With Application to Multisource Navigation

Information fusion is one of the key technologies in airborne multisource navigation, where the federated filter is widely used due to its simple structure. However, the sensor measurement of multisource navigation system will become unreliable in some interferential environment, which leads to the state and measurement noise covariance matrix (MNCM) change over time and estimate difficultly. Inaccurate covariance matrices cause the loss of positioning accuracy in traditional federated filter. To address the problems, this article puts forward an adaptive federal Kalman filtering algorithm based on variational Bayesian and applies it to the inertial navigation system (INS)/global positioning system (GPS)/terrain/geomagnetic multisource navigation system. Simulation and experimental results show that the proposed filter outperforms the traditional federated filter in improving the positioning accuracy under the condition of time-varying or even unknown state and MNCM, which plays a positive role in unmanned aerial vehicle (UAV) multisource navigation.