Robust M-Estimation-Based ICKF for GNSS Outlier Mitigation in GNSS/SINS Navigation Applications

The accuracy and robustness of tightly coupled GNSS/SINS integrated navigation parameter estimation are hindered by outliers caused by GNSS measurements under multipath interference and non-line-of-sight (NLOS) conditions. To address the limitations of robust estimation and the mismatch of state posterior PDF approximation, a novel M-estimation-based robust iterated cubature kalman filter (ICKF) is developed to minimize the impact of GNSS outliers while improving the correction effect of high-quality LOS GNSS measurement in this paper. The nonlinear weighted least squares regression model and objective function are established for outlier mitigation, including a sigma-point iterative filtering framework and an M-estimation method of down-weighting GNSS measurements. Specifically, the maximum a posteriori (MAP) estimation idea is used to design the sigma-point iterated filtering framework, which makes the posteriori probability density function (PDF) constantly close to the high likelihood confidence interval to obtain excellent nonlinear approximation effects. Furthermore, to avoid the inherent non-convexity of the traditional M-estimation method, the relaxation compensation scheme based on the adaptive control factor is introduced into the robust loss function to accelerate the convergence of the penalty weight of GNSS outliers. The availability of the proposed method for GNSS outlier mitigation is proved by vehicle field tests under multipath interference and NLOS conditions.