Resilient multipath prediction and detection architecture for low‐cost navigation in challenging urban areas
Ivan Smolyakov, Mohammad Rezaee, Richard B. Langley
Abstract
A twofold architecture based on GNSS multipath environment prediction and detection is presented in a context of loosely coupled and tightly coupled IMU/GNSS integration for navigation in urban areas. A signal quality monitoring group of techniques is applied for a platform self-contained effort to detect and exclude multipath-contaminated GNSS signals. Additionally, the sensor integration Kalman filter stochastic model is adjusted on-the-fly based on a GNSS multipath environment map. The map is populated by crowdsourcing and contains the spatial distribution of average carrier-to-noise-density ratio measurements, linked to the probability of non-line-of-sight, multipath-contaminated, diffracted, and attenuated signal reception. To address the map availability issue, a random forest machine learning model is developed to propagate the map to the city areas not directly surveyed by the mapping fleet based on open-access geographic data. The architecture performance is evaluated in the automotive scenario showing 13-17% accuracy improvement compared to a conventional Kalman filter.