An Improved Variational Bayesian-Based Adaptive Federated Kalman Filter for Multi-Sensor Integrated Navigation Systems

Efficient fusion of navigation sensor data with different output frequencies and data types is critical for ensuring that vehicle-mounted integrated navigation systems consistently provide stable, reliable navigation solutions in complex dynamic operational environments. To address the degradation of estimation accuracy caused by the noise characteristics mismatch of sensor measurement, an information fusion framework based on federated Kalman filter (FKF) framework is designed by incorporating an improved variational Bayesian-based adaptive Kalman filter (IVBAKF) as the core estimation module of local filters. IVBAKF mitigates the impact of uncertain measurement noise from navigation sensors through effectively estimating the measurement noise covariance matrix (MNCM) by leveraging an adaptive forgetting factor. The adjustment strategy for the forgetting factor employs a predefined mapping function derived from the squared Mahalanobis distance (SMD) of the measurement innovation, which serves as an indicator for detecting anomalies in measurement noise within the FKF, thereby enhancing the tracking capability for the MNCMs. The effectiveness of the proposed algorithm is validated through Monte Carlo simulation-based comparative experiments. The simulation results demonstrate that compared to the FKF-based baseline algorithm with nominal covariance matrices, the proposed algorithm achieves an average reduction of 43.21% in the Root Mean Square Errors (RMSEs) of the estimated navigation parameters in scenarios characterized by uncertain and time-varying measurement noise. Thus, the robustness of the proposed algorithm against complex measurement noise conditions is verified.