Variational Bayesian adaptive high‐degree cubature Huber‐based filter for vision‐aided inertial navigation on asteroid missions

Vision‐aided inertial navigation (VAIN) is a prospective technique for determining the pose of the spacecraft during asteroid missions. The VAIN system can fuse the inertial and visual data by employing the high‐degree cubature Kalman filter (HCKF) because it can accurately handle non‐linear problems. However, the visual measurements can be corrupted by non‐Gaussian noise with unknown time‐varying covariance, resulting in severe degradation of the HCKF. To improve the navigational accuracy of the spacecraft in these situations, the authors propose a novel adaptive robust HCKF known as variational Bayesian (VB) adaptive high‐degree cubature Huber‐based filter (VB‐AHCHF). In the novel algorithm, the fifth‐degree cubature rule and VB theory are combined to estimate the state and track the non‐stationary statistical characteristics of the measurement noise. In addition, utilising the M‐estimation, which is defined as the Huber technique, it modifies the update step of the formal Bayesian filtering. Therefore, the VB‐AHCHF can exhibit adaptability and robustness to the covariance uncertainty and non‐Gaussianity of the measurement noise. Their simulation results show that the estimation accuracy of VB‐AHCHF, as well as its adaptability and robustness, is superior to all state‐of‐the‐art algorithms, e.g. HCKF, high‐degree cubature Huber‐based filter, and the VB adaptive HCKF.