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.

翻译：伪距误差是GPS定位不准确的根本原因。以往的数据驱动方法使用手工设计的中间标签来回归并消除伪距误差。与此不同，我们提出了一种端到端的GPS定位框架E2E-PrNet，该框架利用GPS接收机状态真值计算得到的最终任务损失，直接训练用于伪距校正的神经网络（PrNet）。该损失函数相对于可学习参数的梯度通过可微分的非线性最小二乘优化器反向传播至PrNet。利用安卓手机采集的GPS数据验证了该方法的可行性，结果表明E2E-PrNet优于最先进的端到端GPS定位方法。