基于运动单站的LTE辐射源定位技术

基于天顶对流层延迟（ZTD）的强时空特征，提出了一种融合卷积神经网络的改进注意力机制（CNN-ATT）的多站点ZTD组合预测模型。该模型首次将多源数据（包括日解算精度、年积日(DOY) 和三维坐标）综合运用于ZTD预测任务。通过对南宁市的5个参考站（CORS）和14个国际GNSS服务（IGS）站点共1 501个年积日的观测数据进行研究，选取传统BP模型、GPT2w模型和ATT模型作为基线模型进行实验对比分析。研究结果显示，在预测精度方面，改进的CNN-ATT模型与BP模型相比其均方误差（MSE）和平均绝对误差（MAE）分别减少了5.5 mm和 4.4 mm，预测精度分别提高了41.4%和67.8%；与ATT模型相比，CNN-ATT模型的预测MSE和MAE也分别减少了4.6 mm和2.1 mm，预测精度分别提升了36.2%和50.0%。在定位精度方面，改进的CNN-ATT模型的精度表现优于SAAS，GPT2w，BP以及ATT模型。并且与传统SAAS对流层模型相比，CNN-ATT模型在N，E，U 3个方向的精度提升高达18.2%，12.6%和31.0%。此外，研究还发现CNN-ATT模型在长预测时间步长中的精度表现更为稳定，更适合多测站预测任务，并且其精密单点定位（PPP）收敛速度更快。

LTE radiation source positioning technology based on mobile single station

Based on the strong spatiotemporal characteristics of zenith tropospheric delays (ZTD), a multi-site ZTD combination prediction model with an improved attention mechanism based on convolutional neural networks (CNN-ATT) is proposed. The model integrates multiple data sources, including daily estimation accuracy, day of the year (DOY), and three-dimensional coordinates, for the first time in ZTD prediction tasks. A study is conducted using observation data from 5 reference stations (CORS) in Nanning and 14 International GNSS Service (IGS) stations, spanning a total of 1 501 DOY. Traditional back propagation (BP) models, global pressure and temperature 2wet (GPT2w) models, and ATT models are selected as baseline models for comparative analysis. The prediction results demonstrate that in terms of prediction accuracy, the improved CNN-ATT model outperforms traditional BP neural network models, with a reduction in mean squared error (MSE) and mean absolute error (MAE) by 5.５ mm and 4.４ mm respectively, leading to an improvement in prediction accuracy by 41.４% and 67.8%. Compared to the ATT model, the improved CNN-ATT model also shows reductions in MSE and MAE by 4.6 mm and 2.1 mm, respectively, resulting in a 36.2% and 50.0% enhancement in prediction accuracy. Regarding positional accuracy, the improved CNN-ATT model outperforms the SAAS, GPT2w, BP, and ATT model. Furthermore, when compared to the traditional SAAS tropospheric model, the CNN-ATT model achieves noteworthy accuracy improvements in the N, E and U directions, with enhancements of 18.2%, 12.6% and 31.0% respectively. Additionally, the research unveils that the CNN-ATT model exhibits a more stable performance in extended prediction time steps, making it particularly suitable for multi-station prediction tasks. Moreover, it manifests a faster convergence rate in precise point positioning (PPP) applications.