随着全球卫星导航（Global Navigation Satellite System, GNSS）技术的发展，得益于可用卫星数量的增加和更优的几何分布，多星座全球导航卫星系统定位能为用户提供更为精确的定位结果。近期研究表明，基于因子图优化（Factor-graph Optimization, FGO）的多系统GNSS定位相较于传统算法呈现出更好的性能。然而，基于FGO的多GNSS定位随机模型的精细化及系统间自适应定权问题仍未充分研究。本文提出了一种基于赫尔默特方差分量估计（Helmert Variance Component Estimation, HVCE）的多系统GNSS定位因子图优化方法，通过系统间自适应定权进一步提升复杂城市环境中基于因子图优化的多系统GNSS定位的性能。此外，本文利用抗差算法IGG-III进一步提高了HVCE及FGO状态估计的鲁棒性。在城市环境中的车载测试结果表明，相对于单一FGO方案，本文提出的方法在北向、东向和垂直方向上的多系统GNSS定位精度分别提高了35.4%、8.7%和25.1%。总体而言，本文所提出的方法通过精细化随机模型并实现系统间自适应定权，能够在因子图优化方法的基础上进一步提升复杂城市环境中多系统GNSS的定位性能。

With the development of Global Navigation Satellite System (GNSS), multi-GNSS positioning has been shown to provide a more effective solution for accurate localization, benefiting from an increased number of available satellites and improved geometric distribution. Recent research indicates that Factor Graph Optimization (FGO)-based multi-GNSS positioning outperforms traditional algorithm. However, the refinement of the stochastic model and the adaptive weighting of FGO-based multi-GNSS positioning remain underexplored. To fill this gap, we propose a robust Helmert Variance Component Estimation (HVCE) method to further enhance the performance of multi-GNSS positioning in challenging urban scenarios by adaptive weighting for multi-GNSS. Additionally, the IGG-III robust algorithm is applied to improve both the robust-ness of the HVCE method and the state estimation within the FGO framework. The results of vehicle-borne tests show that positioning accuracy is improved by 35.4%, 8.7%, and 25.1% in the north, east, and vertical directions, respectively. Over-all, the proposed algorithm is validated to be an effective approach to improving multi-GNSS performance in complex ur-ban environments by refining the stochastic model and adaptively weighting the measurements.