车载GNSS/SINS/里程计分布式弹性融合导航方法

To improve the fault-tolerance of a low-cost land vehicle navigation system in the complex environment, this paper proposes a distributed GNSS/SINS/odometer resilient fusion method based on the suboptimal gain fusion algorithm. First, a velocity compensation model for each odometer on four wheels is established according to the Ackermann steering geometry, which improves the accuracy of forward and lateral velocity measurement at the inertial measurement unit center. Then, a fault detection and classification criteria based on Chi-square test statistics is designed to make full use of the available observation information. Last, a resilient adjustment model for the stochastic model and information sharing factors (ISF) are proposed to mitigate the influence of abnormal observation from the sensor layer and the decision layer respectively and realize the resilient fusion of multi-source information. A real car test is carried out to verify the effectiveness of the distributed GNSS/SINS/odometer resilient fusion method. The experiment results demonstrate that the proposed method can effectively reduce the impact of subsystem faults on the global state estimation and improve the fault tolerance performance of the system in complex environments. Moreover, compared with the traditional federated Kalman filtering (FKF) , the SGF algorithm can achieve the equivalent accuracy with significant computational efficiency improvement, which is conducive to the practical engineering application of multi-source information resilient fusion.