In urban environments, where line-of-sight signals from GNSS satellites are
frequently blocked by high-rise objects, GNSS receivers are subject to large
errors in measuring satellite ranges. Heuristic methods are commonly used to
estimate these errors and reduce the impact of noisy measurements on
localization accuracy. In our work, we replace these error estimation
heuristics with a deep learning model based on Graph Neural Networks.
Additionally, by analyzing the cost function of the multilateration process, we
derive an optimal method to utilize the estimated errors. Our approach
guarantees that the multilateration converges to the receiver's location as the
error estimation accuracy increases. We evaluate our solution on a real-world
dataset containing more than 100k GNSS epochs, collected from multiple cities
with diverse characteristics. The empirical results show improvements from 40%
to 80% in the horizontal localization error against recent deep learning
baselines as well as classical localization approaches.

我们的研究替代了启发式方法，使用基于图神经网络的深度学习模型来估计大城市环境下卫星测距误差，并分析了多次叠加的最优方法，提高了水平定位误差的准确性。实证结果显示与最近的深度学习基线模型和经典定位方法相比，我们的解决方案在水平定位误差上有 40% 至 80% 的改善。

利用成本函数调节的多基站测距和图神经网络的 GNSS 定位

GNSS Positioning using Cost Function Regulated Multilateration and Graph  Neural Networks

Jamming signals can jeopardize the operation of GNSS receivers until denying
its operation. Given their ubiquity, jamming mitigation and localization
techniques are of crucial importance, for which jammer classification is of
help. Data-driven models have been proven useful in detecting these threats,
while their training using crowdsourced data still poses challenges when it
comes to private data sharing. This article investigates the use of federated
learning to train jamming signal classifiers locally on each device, with model
updates aggregated and averaged at the central server. This allows for
privacy-preserving training procedures that do not require centralized data
storage or access to client local data. The used framework FedAvg is assessed
on a dataset consisting of spectrogram images of simulated interfered GNSS
signal. Six different jammer types are effectively classified with comparable
results to a fully centralized solution that requires vast amounts of data
communication and involves privacy-preserving concerns.

本文研究使用联合学习在每个设备上本地训练干扰信号分类器以进行隐私保护培训程序，以解决私人数据共享的挑战，并成功将六种不同类型的干扰源分类，结果可与需要大量数据通信的完全集中化的解决方案相比拟。