An improved multiple-outlier-robust GNSS/INS EKF filer based on multiple statistical similarity measure

Abstract High-precision and highly reliable location information is the key support for autonomous driving applications. Global Navigation satellite system (GNSS)/Inertial navigation system (INS) integrated positioning is a typical high-precision positioning technology. However, satellite signals are easily affected by various interferences such as signal blocking and multipath problems, leading to performance degradation in complex urban environments. The traditional extended Kalman filtering (EKF) cannot handle observation outlier, and the system’s positioning accuracy sharply declines when the observations are affected by non-Gaussian heavy-tailed noise. Therefore, this paper presents an improved multiple-outlier-robust extended Kalman filter (I-MOREKF) for GNSS/INS Loosely Coupled Integration. A multiple statistical similarity measure (MSSM) is built to evaluate the similarity between two random vectors from dimension to dimension. Then, the I-MOREKF is proposed by maximizing a cost function based on the MSSM, and considering the robust estimation with IGG III model. The proposed model effectively removes multiple types of errors in complex environments. A vehicular test was carried out to validate the feasibility and performance of the proposed model. Simulated random gross errors are introduced in GNSS measurements, and the measurement noises are corrupted by heavy-tail noise. The results show that simulated gross errors can be successfully detected with I-MOREKF model, and the 3D position Root Meas Square Error is 0.0148 m for I-MOREKF, which are improved by 97.2%,13.5% and 52.9%, as compared with EKF, IGG Ⅲ and MOREKF.