Incrementally Learned Mixture Models for GNSS Localization
This addresses non-Gaussian error modeling for autonomous systems without prior knowledge, though it is incremental as it builds on existing variational methods.
The paper tackles the problem of GNSS localization errors from non-line-of-sight effects by introducing an incremental variational Bayesian algorithm that learns Gaussian mixture models online, demonstrating superior accuracy on real-world datasets.
GNSS localization is an important part of today's autonomous systems, although it suffers from non-Gaussian errors caused by non-line-of-sight effects. Recent methods are able to mitigate these effects by including the corresponding distributions in the sensor fusion algorithm. However, these approaches require prior knowledge about the sensor's distribution, which is often not available. We introduce a novel sensor fusion algorithm based on variational Bayesian inference, that is able to approximate the true distribution with a Gaussian mixture model and to learn its parametrization online. The proposed Incremental Variational Mixture algorithm automatically adapts the number of mixture components to the complexity of the measurement's error distribution. We compare the proposed algorithm against current state-of-the-art approaches using a collection of open access real world datasets and demonstrate its superior localization accuracy.