Global navigation satellite system (GNSS) positioning is widely used for urban navigation, but the covariance reported by the GNSS solver is often unreliable in urban canyons. Existing differentiable factor graph optimization (DFGO) methods learn measurement weighting through the solver, but they still use position-only objectives. As a result, the position estimate may improve while the reported covariance remains too small, too large, or incorrectly oriented. We propose CredibleDFGO (CDFGO), a differentiable GNSS factor graph framework that makes covariance credibility an explicit training target. A Weighting Generation Network (WGN) predicts per-satellite reliability weights, and a differentiable Gauss-Newton solver maps these weights to a position estimate and a Hessian-derived posterior covariance. We use proper scoring rules to supervise the East-North predictive distribution end to end. We study negative log-likelihood (NLL), the energy score (ES), and their combination. Results on three UrbanNav test scenes show consistent gains in covariance credibility. Positioning accuracy also improves on the medium-urban and harsh-urban scenes; on the deep-urban scene, both the mean horizontal error and the 95th-percentile error improve. On the harsh-urban Mong Kok (MK) scene, CDFGO-Combined reduces the mean horizontal error from 13.77 m to 11.68 m, reduces NLL from 40.63 to 6.59, and reduces ES from 12.31 to 9.05 relative to DFGO (MAE). Case studies link the MK improvement to better axis-wise consistency, more credible local covariance ellipses, and satellite-level reweighting.

翻译：全球导航卫星系统（GNSS）定位被广泛用于城市导航，但在城市峡谷中，GNSS解算器报告的协方差往往不可靠。现有的可微因子图优化（DFGO）方法通过解算器学习测量权重，但仍仅使用位置目标。这可能导致位置估计改善，但报告协方差仍然过小、过大或方向错误。我们提出可信DFGO（CDFGO），一种可微GNSS因子图框架，将协方差可信度作为显式训练目标。权重生成网络（WGN）预测每颗卫星的可靠性权重，可微高斯-牛顿解算器将这些权重映射为位置估计和基于Hessian的后验协方差。我们使用恰当评分规则端到端监督东-北预测分布。我们研究了负对数似然（NLL）、能量评分（ES）及其组合。在三个UrbanNav测试场景上的结果显示了协方差可信度的一致性提升。定位精度在中度城市和恶劣城市场景上也有所提升；在深度城市场景中，平均水平误差和95百分位误差均有改善。在恶劣城市的旺角（MK）场景上，相对于DFGO（MAE），CDFGO-Combined将平均水平误差从13.77米降至11.68米，NLL从40.63降至6.59，ES从12.31降至9.05。案例研究将MK改进归因于更好的轴一致性、更可信的局部协方差椭圆以及卫星级重加权。