Global Navigation Satellite System (GNSS) has been the most popular tool for providing positioning, navigation, and timing (PNT) information. Some methods have been developed for enhancing the GNSS performance in signal challenging environments (urban canyon, dense foliage, signal blockage, multipath, and none-line-of-sight signals). Vector Tracking Loop (VTL) was recognized as the most promising and prospective one among these technologies, since VTL realized mutual aiding between channels. However, momentary signal blockage from part of the tracking channels affected the VTL operation and the navigation solution estimation. Moreover, insufficient available satellites employed would lead to the navigation solution errors diverging quickly over time. Short-time or temporary signal blockage was common in urban areas. Aiming to improve the VTL performance during the signal outage, in this paper, the deep learning method was employed for assisting the VTL navigation solution estimation; more specifically, a Long Short-Term Memory-Recurrent Neural Network (LSTM-RNN) was employed to aid the VTL navigation filter (navigation filter was usually a Kalman filter). LSTM-RNN obtained excellent performance in time-series data processing; therefore, in this paper, the LSTM-RNN was employed to predict the navigation filter innovative sequence values during the signal outage, and then, the predicted innovative values were employed to aid the navigation filter for navigation solution estimation. The LSTM-RNN was well trained while the signal was normal, and the past innovative sequence was employed as the input of the LSTM-RNN. A simulation was designed and conducted based on an open-source Matlab GNSS software receiver; a dynamic trajectory with several temporary signal outages was designed for testing the proposed method. Compared with the conventional VTL, the LSTM-RNN-assisted VTL could keep the horizontal positioning errors within 50 meters during a signal outage. Also, conventional Support Vector Machine (SVM) and radial basis function neural network (RBF-NN) were compared with the LSTM-RNN method; LSTM-RNN-assisted VTL could maintain the positioning errors less than 20 meters during the outages, which demonstrated LSTM-RNN was superior to the SVM and RBF-NN in these applications.

中文翻译：

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LSTM-RNN辅助的矢量跟踪环路，用于信号中断桥接

全球导航卫星系统（GNSS）已经成为提供定位，导航和定时（PNT）信息的最受欢迎的工具。已经开发出一些方法来增强信号挑战性环境（城市峡谷，茂密的树叶，信号阻塞，多径和非视线信号）中的GNSS性能。矢量跟踪环（VTL）被认为是这些技术中最有前途和最有前景的技术，因为VTL实现了通道之间的互助。但是，来自部分跟踪通道的瞬时信号阻塞影响了VTL操作和导航解决方案估计。此外，所采用的可用卫星不足会导致导航解决方案误差随时间迅速分散。短时或暂时的信号阻塞在城市地区很普遍。为了提高信号中断时的VTL性能，本文采用深度学习方法辅助VTL导航解的估计。更具体地说，采用了长期短期记忆递归神经网络（LSTM-RNN）来辅助VTL导航过滤器（导航过滤器通常是卡尔曼过滤器）。LSTM-RNN在时序数据处理中获得了出色的性能；因此，本文采用LSTM-RNN预测信号中断期间的导航滤波器创新序列值，然后，将预测的创新值用于辅助导航滤波器进行导航解估计。在信号正常的情况下，对LSTM-RNN进行了良好的培训，并且将过去的创新序列用作LSTM-RNN的输入。基于开源的Matlab GNSS软件接收器设计并进行了仿真；设计了具有几个临时信号中断的动态轨迹来测试该方法。与传统的VTL相比，LSTM-RNN辅助的VTL在信号中断期间可以将水平定位误差保持在50米以内。此外，将传统的支持向量机（SVM）和径向基函数神经网络（RBF-NN）与LSTM-RNN方法进行了比较；LSTM-RNN辅助的VTL在中断期间可以将定位误差保持在20米以内，这表明LSTM-RNN在这些应用中优于SVM和RBF-NN。LSTM-RNN辅助的VTL可以在信号中断期间将水平定位误差保持在50米以内。此外，将传统的支持向量机（SVM）和径向基函数神经网络（RBF-NN）与LSTM-RNN方法进行了比较；LSTM-RNN辅助的VTL在中断期间可以将定位误差保持在20米以内，这表明LSTM-RNN在这些应用中优于SVM和RBF-NN。LSTM-RNN辅助的VTL可以在信号中断期间将水平定位误差保持在50米以内。此外，将传统的支持向量机（SVM）和径向基函数神经网络（RBF-NN）与LSTM-RNN方法进行了比较；LSTM-RNN辅助的VTL在中断期间可以将定位误差保持在20米以内，这表明LSTM-RNN在这些应用中优于SVM和RBF-NN。