Towards End-to-End GPS Localization with Neural Pseudorange Correction
This addresses GPS localization errors for users of mobile devices, representing an incremental improvement over existing data-driven approaches.
The paper tackles GPS localization inaccuracy by proposing E2E-PrNet, an end-to-end framework that trains a neural network for pseudorange correction directly using the final task loss, outperforming baseline and state-of-the-art methods on GPS data from Android phones.
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.