Robust Sequential Variational Bayesian Filter for Tightly Coupled Navigation System Within GNSS Challenged Environment

Navigation systems are commonly used in Internet of Things (IoT) devices to provide navigation information. For achieving accurate information on devices, this article describes a robust variational Bayesian filter with sequential processing to estimate the time-varying measurement noise covariance and reject the outliers within the GNSS-challenged environment. Different from the variational Bayesian adaptive filter with heavy-tailed distribution, the proposed filter uses the beta-Bernoulli distribution to prevent the outliers from interfering with the time-varying measurement noise covariance estimation by the inverse-Gamma distribution. Then, the sequential processing is modified for the proposed filter in the independent multi-measurement sensor system. Sequential processing variational Bayesian interference is first proposed and applied to the tightly coupled navigation system, so far as the authors know. To evaluate the proposed method, real-time experiments in a downtown area and forest park effectively validated the filter robustness estimation, especially in scenarios with GNSS satellite obstructions.