Browsing by Author "Phillips, Christian Michael"

Now showing 1 - 1 of 1
  • Thumbnail Image
    ItemOpen Access
    Machine Learning for Robust Detection and Mitigation of GNSS Multipath
    (2025-01-07) Phillips, Christian Michael; O'Keefe, Kyle; Broumandan, Ali; Bayat, Sayeh; El-Sheimy, Naser
    Distortion to the correlation function caused by multipath and non-line of sight signals can result in pseudorange errors on the order of several tens of meters in urban canyon environments. To address this problem, a deep learning approach for classifying multipath and estimating observation weights from a global navigation satellite systems (GNSS) receiver correlation function is presented. This approach uses a 1-dimensional convolutional neural network, suitable for embedded applications, to classify the magnitude of pseudorange error associated with correlation functions and to generate a multipath probability used for deriving observation weights. The network is trained and tested on live GNSS data collected in a challenging urban environment. The capability of the model to remove high error measurements for a least-squares positioning solution and to generate weights for a weighted least-squares positioning solution is explored. The network has proven to be effective at detecting measurements with high multipath ranging error, and to effectively generalize to unseen data. The removal of detected measurements reduced positioning error by up to 80%, and the use of deep learning derived weights in the positioning solution reduced positioning error by a further 50%. In both cases, positioning errors were comparable to what is expected for an open-sky single frequency standalone positioning solution.