Towards End-to-End GPS Localization with Neural Pseudorange Correction
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Abstract (arXiv)
The pseudorange error is one of the root causes of localization inaccuracy in GPS. Previous data-driven methods regress and eliminate pseudorange errors using handcrafted intermediate labels. Unlike them, we propose an end-to-end GPS localization framework, E2E-PrNet, to train a neural network for pseudorange correction (PrNet) directly using the final task loss calculated with the ground truth of GPS receiver states. The gradients of the loss with respect to learnable parameters are backpropagated through a Differentiable Nonlinear Least Squares (DNLS) optimizer to PrNet. The feasibility of fusing the data-driven neural network and the model-based DNLS module is verified with GPS data collected by Android phones, showing that E2E-PrNet outperforms the baseline weighted least squares method and the state-of-the-art end-to-end data-driven approach. Finally, we discuss the explainability of E2E-PrNet.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 12 of its 21 references that are in the map. First posted on arXiv 19 Jan 2024.
Builds on
12 of 21 references.
References · 12
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- 2021Improving GNSS Positioning using Neural Network-based Correctionscited by 10
- 2022Neural Moving Horizon Estimation for Robust Flight Controlcited by 5
- 2021Differentiable Moving Horizon Estimation for Robust Flight Controlcited by 4
- 2023Localization with Noisy Android Raw GNSS Measurementscited by 2
- 2023PrNet: A Neural Network for Correcting Pseudoranges to Improve Positioning with Android Raw GNSS Measurementscited by 2
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