# Diff-GNSS: Diffusion-based Pseudorange Error Estimation

arXiv: https://arxiv.org/abs/2509.17397 · HTML: https://mlsanity.com/paper/2509.17397/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 44 references that are in the map.
- First posted on arXiv 22 Sep 2025.

## Builds on (most cited first, 12 of 12)

- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://mlsanity.com/paper/2312.00752/) (2023)
- [SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis](https://mlsanity.com/paper/2307.01952/) (2023)
- [DiT-3D: Exploring Plain Diffusion Transformers for 3D Shape Generation](https://mlsanity.com/paper/2307.01831/) (2023)
- [Diffusion-Based 3D Human Pose Estimation with Multi-Hypothesis Aggregation](https://mlsanity.com/paper/2303.11579/) (2023)
- [DIFFVSGG: Diffusion-Driven Online Video Scene Graph Generation](https://mlsanity.com/paper/2503.13957/) (2025)
- [Fast Training of Diffusion Transformer with Extreme Masking for 3D Point Clouds Generation](https://mlsanity.com/paper/2312.07231/) (2023)
- [Towards Robust GNSS Positioning and Real-time Kinematic Using Factor Graph Optimization](https://mlsanity.com/paper/2106.01594/) (2021)
- [Learning-based NLOS Detection and Uncertainty Prediction of GNSS Observations with Transformer-Enhanced LSTM Network](https://mlsanity.com/paper/2309.00480/) (2023)
- [Machine Learning-Based GPS Multipath Detection Method Using Dual Antennas](https://mlsanity.com/paper/2204.14001/) (2022)
- [Distributional loss for convolutional neural network regression and application to GNSS multi-path estimation](https://mlsanity.com/paper/2206.01473/) (2022)
- [UniMSF: A Unified Multi-Sensor Fusion Framework for Intelligent Transportation System Global Localization](https://mlsanity.com/paper/2409.12426/) (2024)

## Abstract (arXiv)

Global Navigation Satellite Systems (GNSS) are vital for reliable urban positioning. However, multipath and non-line-of-sight reception often introduce large measurement errors that degrade accuracy. Learning-based methods for predicting and compensating pseudorange errors have gained traction, but their performance is limited by complex error distributions. To address this challenge, we propose Diff-GNSS, a coarse-to-fine GNSS measurement (pseudorange) error estimation framework that leverages a conditional diffusion model to capture such complex distributions. Firstly, a Mamba-based module performs coarse estimation to provide an initial prediction with appropriate scale and trend. Then, a conditional denoising diffusion layer refines the estimate, enabling fine-grained modeling of pseudorange errors. To suppress uncontrolled generative diversity and achieve controllable synthesis, three key features related to GNSS measurement quality are used as conditions to precisely guide the reverse denoising process. We further incorporate per-satellite uncertainty modeling within the diffusion stage to assess the reliability of the predicted errors. We have collected and publicly released a real-world dataset covering various scenes. Experiments on public and self-collected datasets show that DiffGNSS consistently outperforms state-of-the-art baselines across multiple metrics. To the best of our knowledge, this is the first application of diffusion models to pseudorange error estimation. The proposed diffusion-based refinement module is plug-and-play and can be readily integrated into existing networks to markedly improve estimation accuracy.