AI 中文总结
该研究提出基于光线追踪的框架，结合贝叶斯优化与ResNet架构，生成合成RSS指纹数据集，在仅用合成数据训练时，其定位误差3.05米，比最优基准提升33.6%，可减少真实RSS指纹的采集需求。
AI 中文摘要
基于Wi-Fi的室内定位通常依赖于大量真实接收信号强度（RSS）指纹的采集，这使得部署成本高昂且耗时。本文提出一种基于光线追踪的框架，通过从建筑模型生成合成RSS指纹来减少对真实数据的依赖。首先，我们通过贝叶斯优化利用少量真实RSS指纹校准建筑模型，随后进行每个接入点的校准以修正模拟RSS值中的残留误差。校准后的模型被用于在任意位置生成大规模的合成RSS指纹增强数据集。为了有效利用这些数据进行定位，我们引入了RSS值的新型二值和多值表示，以及支持2.4GHz与5GHz测量值跨频段融合的基于ResNet的定位架构。我们在真实校园建筑上针对四种不同基准方法评估了所提定位方法。当仅使用合成数据训练时，采用多值表示和上游跨频段融合的所提方法在真实数据测试集上实现了3.05米的平均定位误差，比最优基准方法性能提升了33.6%。结果表明，经过校准的基于光线追踪的模拟可大幅减少对真实RSS指纹的需求，同时实现基于深度学习的精准室内定位。
英文摘要
Indoor localization based on Wi-Fi typically relies on extensive collection of real-world received signal strength (RSS) fingerprints, making deployment costly and time-consuming. We present a ray-tracing-based framework that reduces this reliance by generating synthetic RSS fingerprints from a building model. We first calibrate the building model using a small amount of real RSS fingerprints through Bayesian optimization, followed by per-access-point calibration to account for residual errors in simulated RSS values. The calibrated model is then used to generate a large augmented dataset of synthetic RSS fingerprints at arbitrary locations. To effectively exploit these data for localization, we introduce novel binary and multivalued representations of RSS values and a ResNet-based localization architecture that supports cross-band fusion of 2.4 and 5 GHz measurements. We evaluate our localization method on a real campus building against a diverse set of four baselines. When trained exclusively on synthetic data, the proposed method with multivalued representation and upstream cross-band fusion achieves a mean localization error of 3.05m on a real-data test set, outperforming the best baseline by 33.6%. The results demonstrate that calibrated ray-tracing-based simulation can substantially reduce the need for real RSS fingerprints while enabling accurate deep-learning-based indoor localization.
Comments28 pages
