Towards End-to-End GPS Localization with Neural Pseudorange Correction
- URL: http://arxiv.org/abs/2401.10685v1
- Date: Fri, 19 Jan 2024 13:32:55 GMT
- Title: Towards End-to-End GPS Localization with Neural Pseudorange Correction
- Authors: Xu Weng, KV Ling, Haochen Liu, Kun Cao
- Abstract summary: 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 feasibility is verified with GPS data collected by Android phones, showing that E2E-PrNet outperforms the state-of-the-art end-to-end GPS localization methods.
- Score: 7.127439652247243
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Pseudorange errors are the root cause 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 optimizer to PrNet. The feasibility is verified with GPS data
collected by Android phones, showing that E2E-PrNet outperforms the
state-of-the-art end-to-end GPS localization methods.
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