A Student's T Distribution-Based Filter Design for SINS/GNSS With Heavy-Tailed Noise

This paper presents an enhanced robust filtering algorithm designed for integrated SINS/GNSS navigation systems operating under nonGaussian noise conditions. To address the challenges posed by heavy-tailed noise distributions, a novel noise modelling framework based on Student's t-distribution is developed, which provides superior outlier resilience compared to conventional Gaussian assumptions. Furthermore, a Gaussian mixture model representation is employed for both the one-step predicted and likelihood probability density functions, enabling more accurate quantification of uncertainty. Additionally, a variational Bayesian-based adaptive mechanism is employed for dynamic scale matrix optimisation, effectively mitigating the impact of process noise outliers. Extensive experimental validation, including Monte Carlo simulations and vehicular tests, demonstrates the algorithm's superior performance in SINS/GNSS integration scenarios. Comparative results indicate significant improvements in positioning accuracy and robust convergence characteristics relative to a decent number of iterations.