Information Monitoring and Adaptive Information Fusion of Multi-Source Fusion Navigation Systems in Complex Environments

Accurately obtaining the navigation information of the device is crucial for realizing various emerging Internet of Things (IoT) applications, and a multi-source fusion navigation system is the key to achieving this goal. A distributed integrated inertial navigation system (INS), polarization compass (PC), and geomagnetic compass (MAG) enhanced direction approach is presented to improve the accuracy and robustness of the multisource fusion navigation system in complex environments. To estimate the time-varying measurement noise covariance in a nonlinear multi-source fusion navigation system, the traditional federated Kalman filter (FKF) is improved. In the FKF framework, the third-order spherical radial cubature rule and variational Bayesian theory are introduced, and a variational Bayesian federated cubature Kalman filter (VBFCKF) is proposed. Furthermore, a distributed information monitoring and compensation algorithm based on residuals is developed to address issues like anomalous measured values and asynchronous multi-rate problems. Finally, an experimental platform for unmanned vehicle navigation is designed, and the tests are conducted to confirm the efficacy of the suggested approach. The experimental results show that the system can precisely estimate values based on the measurement quality of sub-filters during navigation. It effectively adjusts measurement noise covariance during updates, thereby mitigating the negative impact of interferences like occlusions and electromagnetic noise on the multi-source fusion navigation system in complex environments. This can strengthen the accuracy and robustness of the navigation system.