This paper builds on the machine learning research to propose two new algorithms based on optimizing the Adaptive Neuro Fuzzy Inference System (ANFIS) with a dual‐polarization antenna to predict pseudorange errors by considering multiple variables including the right‐hand circular polarized (RHCP) signal strength, signal strength difference between the left‐hand circular polarized (LHCP) and RHCP outputs, satellites’ elevation angle, and pseudorange residuals. The final antenna position is calculated following the application of the predicted pseudorange errors to correct for the effects of non‐line‐of‐sight (NLOS) and multipath signal reception. The results show that the proposed algorithm results in a 30% improvement in the root mean square error (RMSE) in the 2D (horizontal) component for static applications when the training and testing data are collected at the same location. This corresponds to 13% to 20% when the testing data is from locations away from that of the training dataset.

中文翻译：

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将双极化GPS天线与优化的自适应神经模糊推理系统配合使用，以提高城市峡谷中的单点定位精度

本文在机器学习研究的基础上，提出了两种新算法，它们基于双极化天线优化自适应神经模糊推理系统（ANFIS），通过考虑包括右旋圆极化（RHCP）信号在内的多个变量来预测伪距误差强度，左圆极化（LHCP）和RHCP输出之间的信号强度差，卫星的仰角和伪距残差。在应用预测的伪距误差以校正非视距（NLOS）和多径信号接收的影响之后，计算最终天线位置。结果表明，当在相同位置收集训练和测试数据时，所提出的算法可将静态应用的2D（水平）分量的均方根误差（RMSE）提高30％。当测试数据来自远离训练数据集的位置时，这相当于13％到20％。