GNSS/SINS/UWB collaborative navigation algorithm based on VBAKF

Collaborative navigation is one of the key technologies for the swarm flight of unmanned aerial vehicles. To address the problem that in the Strapdown Inertial Navigation System (SINS)/Global Navigation Satellite System (GNSS)/Ultra Wide Band ranging system (UWB), measurement noise and system noise are easily affected under the influence of complex environmental factors, which leads to unknown characteristics of measurement noise and further reduces positioning accuracy, a collaborative navigation algorithm based on Variational Bayesian Adaptive Kalman Filter (VBAKF) is proposed. First, a collaborative navigation algorithm is derived based on the GNSS/SINS tightly coupled integration. Secondly, to reduce the impact of complex environmental factors on collaborative accuracy, a collaborative navigation algorithm based on a Variational Bayesian Adaptive Kalman Filter (VBAKF) is proposed. The results of simulation experiments show that in the collaborative navigation system, the algorithm can effectively track the estimated system states and their covariance. Compared with the traditional adaptive Kalman filter (AKF), the VBAKF method improves positioning accuracy by 12.8%, 47.4% and 37.1% in the X, Y, and Z directions of the ECEF coordinate system, respectively, indicating that the VBAKF method significantly improves the accuracy and reliability of system state estimation.