We present a neural network for mitigating biased errors in pseudoranges to improve localization performance with data collected from mobile phones. A satellite-wise Multilayer Perceptron (MLP) is designed to regress the pseudorange bias correction from six satellite, receiver, context-related features derived from Android raw Global Navigation Satellite System (GNSS) measurements. To train the MLP, we carefully calculate the target values of pseudorange bias using location ground truth and smoothing techniques and optimize a loss function involving the estimation residuals of smartphone clock bias. The corrected pseudoranges are then used by a model-based localization engine to compute locations. The Google Smartphone Decimeter Challenge (GSDC) dataset, which contains Android smartphone data collected from both rural and urban areas, is utilized for evaluation. Both fingerprinting and cross-trace localization results demonstrate that our proposed method outperforms model-based and state-of-the-art data-driven approaches.

翻译：我们提出一种神经网络，用于削弱伪距中的有偏误差，以提升基于手机采集数据的定位性能。设计了一种面向卫星的多层感知机（MLP），该网络基于从安卓原始全球导航卫星系统（GNSS）测量中提取的六个卫星端、接收端及环境相关特征，回归伪距偏差修正量。为训练该MLP，我们利用位置真值与平滑技术精心计算伪距偏差的目标值，并优化涉及智能手机时钟偏差估计残差的损失函数。修正后的伪距随后被基于模型的定位引擎用于计算位置。采用谷歌智能手机分米挑战赛（GSDC）数据集（包含城乡区域采集的安卓智能手机数据）进行评估。指纹定位与跨轨迹定位的实验结果均表明，所提方法优于基于模型的方法及现有最佳数据驱动方法。