Graph Transformer for GNSS NLOS Signal Detection via Dynamic Environmental Representation

ZENG Kungan; TANG Jianhao; LI Zhenni; ZHENG Shaolong; XIE Shengli

doi:10.13203/j.whugis20260106

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Graph Transformer for GNSS NLOS Signal Detection via Dynamic Environmental Representation

CPGNSS 25th Anniversary Column | 更新时间：2026-11-05

- Graph Transformer for GNSS NLOS Signal Detection via Dynamic Environmental Representation
- Geomatics and Information Science of Wuhan University Vol. 51, Issue 7, Pages: 1299-1312(2026)
- 作者机构：
  
  1.广东工业大学自动化学院，广东广州，510006
  2.广东省智能系统与优化集成重点实验室，广东广州，510006
  3.粤港澳离散制造智能化联合实验室，广东广州，510006
  4.物联网智能信息处理与系统集成教育部重点实验室，广东广州，510006
  5.深空探测实验室，北京，100084
- 作者简介：
- 基金信息：
- DOI：10.13203/j.whugis20260106
  CLC： P228
- Received：01 April 2026，
  
  Published：05 July 2026
- 稿件说明：
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曾昆赣,唐健浩,李珍妮，等.面向动态环境表征的图Transformer GNSS非视距信号检测方法[J].武汉大学学报（信息科学版）,2026,51(7):1299-1312.

ZENG Kungan,TANG Jianhao,LI Zhenni,et al.Graph Transformer for GNSS NLOS Signal Detection via Dynamic Environmental Representation[J].Geomatics and Information Science of Wuhan University,2026,51(7):1299-1312.
曾昆赣,唐健浩,李珍妮，等.面向动态环境表征的图Transformer GNSS非视距信号检测方法[J].武汉大学学报（信息科学版）,2026,51(7):1299-1312. DOI：10.13203/j.whugis20260106

ZENG Kungan,TANG Jianhao,LI Zhenni,et al.Graph Transformer for GNSS NLOS Signal Detection via Dynamic Environmental Representation[J].Geomatics and Information Science of Wuhan University,2026,51(7):1299-1312. DOI：10.13203/j.whugis20260106

摘要

在低空经济快速发展的背景下，无人机在城市复杂环境中的物流配送、应急救援及巡检等任务对其连续、高精度的定位能力提出了严峻挑战。在高楼密集的城市峡谷环境中，全球导航卫星系统遭受严重的非视距（non-line-of-sight，NLOS）信号干扰，易导致定位误差增大甚至跳变，威胁无人机飞行安全。现有的深度神经网络检测方法在处理该问题时仍面临两大挑战：一是输入通常需固定维度，对可变数量观测值的零填充会引入非物理意义的伪观测；二是模型在不同时空场景下的泛化能力不足，难以适应动态变化的低空环境。为此，提出一种融合边特征编码与稀疏适配器的图Transformer检测模型。首先，构建天空卫星图，将历元内的卫星观测数据转化为图结构输入，在灵活表征卫星间几何拓扑关系的同时，避免了强制对齐维度带来的数据失真。其次，设计了一种节点-边联合注意力机制（graph node-edge attention，GNE-Attention），通过边特征动态调节卫星间的相似度评分，实现对环境特征的自适应提取。此外，设计了一个稀疏适配器，使卫星能够从GNE-Attention的输出中筛选关键的环境表征信息，从而显著增强了模型在异构场景和多径环境下的检测泛化能力。在真实城市峡谷数据集上的实验结果表明，所提方法在NLOS信号检测任务中的准确率超过93%，并且相较于各类现有神经网络方法，准确率提升约3%~20%。跨时段与跨区域的测试进一步验证了该方法在复杂环境中的稳健性，为无人机安全飞行提供了可靠的定位技术支撑。

Abstract

Objectives

In the context of the rapidly developing low-altitude economy

unmanned aerial vehicle (UAV) are widely deployed for tasks such as urban logistics

emergency response

and infrastructure inspection

which impose stringent requirements for continuous and high-precision positioning in complex urban environments. In dense urban canyons

global navigation satellite system (GNSS) signals suffer from severe non-line-of-sight (NLOS) interference due to low flight altitudes and restricted sky visibility. This frequently leads to increased positioning errors and abrupt positioning jumps

threatening the safety and reliability of UAV operations. Although deep neural network-based methods have achieved progress in NLOS detection

two critical challenges remain. First

existing approaches typically require fixed-dimension inputs

necessitating zero-padding for variable numbers of satellite observations

which introduces non-physical pseudo-observations. Second

these models exhibit insufficient generalization across different spatiotemporal scenarios

making it difficult to adapt to the rapidly changing low-altitude environments encountered during UAV flight.

Methods

An edge-feature-encoded graph Transformer with sparse adapter (EGTSA) model is proposed to address the generalization challenges of GNSS NLOS detection. To avoid the distortion caused by forced dimension alignment

satellites observed within the same epoch are organized into a sky satellite graph. This allows variable-length GNSS measurements to be naturally represented through graph structures while flexibly characterizing inter-satellite geometric topological relationships. To enhance environmental representation learning

a graph node-edge attention (GNE-Attention) mechanism is introduced. By utilizing edge features to dynamically adjust inter-satellite similarity scores

the model can adaptively extract discriminative environmental features. Furthermore

a sparse adapter is designed to enable satellites to selectively attend to key environmental representations from the GNE-Attention output

thereby improving generalization in dynamic multipath environments.

Results

Extensive experiments were conducted using real-world GNSS datasets collected in complex urban canyon environments at different times and locations. The experimental results demonstrate that the proposed EGTSA model achieves NLOS detection accuracies exceeding 93%

representing an improvement of approximately 3% to 20% compared with existing state-of-the-art methods. Furthermore

cross-time and cross-location tests verify the robustness of the method in complex low-altitude urban environments

demonstrating its strong generalization ability.

Conclusions

A graph Transformer-based NLOS detection framework is proposed to address the issues of variable-length satellite measurements and poor cross-domain generalization. By modeling satellites as graph nodes and explicitly encoding geometric relationships via GNE-Attention

the proposed method preserves the intrinsic structure of GNSS observations and achieves adaptive environmental feature extraction. The superior detection accuracy and strong generalization performance across different urban environments

times

and locations provides reliable technical support for the stable and safe GNSS positioning of low-altitude UAVs.

关键词

Keywords

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