Global navigation satellite system (GNSS) receivers meet numerous challenges in a high-orbit environment, including weak and discontinuous signal, and time-varying strength. To resolve these issues and enhance reliability, an innovative adaptive vector tracking loop (VTL) scheme is proposed. Non-linear models of the VTL filter are established to calculate code phase and carrier frequency errors accurately. Based on this, a deep analysis has been developed on the measurement noise. To reduce the impact of the interdependent noises among channels in VTL, an adaptive VTL algorithm assisted by the variational Bayesian (VB) learning network is proposed to estimate the measurement noise and maintain the error convergence in the time-varying noise or signal outage conditions. Further, the implementation steps of the adaptive algorithm have been designed in detail. In particular, the carrier-to-noise power ratio (C/N0) estimation method is further employed to update the a prior probability density in case of change of tracking satellite. The simulation results indicate that the proposed VTL scheme with VB algorithm is a promising method to improve the accuracy and reliability of GNSS receivers significantly under a high-orbit degraded signal environment.

中文翻译：

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一种高轨退化GNSS信号的自适应矢量跟踪方案

全球导航卫星系统 (GNSS) 接收器在高轨道环境中面临许多挑战，包括微弱和不连续的信号，以及随时间变化的强度。为了解决这些问题并提高可靠性，提出了一种创新的自适应矢量跟踪环（VTL）方案。建立VTL滤波器的非线性模型，准确计算码相位和载波频率误差。在此基础上，对测量噪声进行了深入分析。为了减少VTL中通道间相互依赖的噪声的影响，提出了一种由变分贝叶斯（VB）学习网络辅助的自适应VTL算法来估计测量噪声并在时变噪声或信号中断条件下保持误差收敛。进一步，详细设计了自适应算法的实现步骤。特别是，载波噪声功率比（C / N0) 估计方法进一步用于在跟踪卫星发生变化的情况下更新先验概率密度。仿真结果表明，所提出的VB算法VTL方案是一种很有前途的方法，可以在高轨道退化信号环境下显着提高GNSS接收机的精度和可靠性。