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An Adaptive Vector Tracking Scheme for High-Orbit Degraded GNSS Signal

机译:高轨道降解GNSS信号的自适应矢量跟踪方案

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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/N-0) 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.
机译:全球导航卫星系统(GNSS)接收器在高轨道环境中遇到许多挑战,包括弱和不连续的信号,以及时变强度。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路(VTL)方案。建立VTL滤波器的非线性模型以精确计算代码相位和载波频率误差。基于此,在测量噪声上开发了深度分析。为了减少VTL中的信道之间的相互依存噪声的影响,提出了由变分贝叶斯(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会聚。此外,已经详细设计了自适应算法的实现步骤。特别地,进一步采用载波对噪声功率比(C / N-0)估计方法来更新跟踪卫星的变化情况下的先前概率密度。仿真结果表明,具有VB算法的提议的VTL方案是提高GNSS接收器在高轨道下降的信号环境下显着提高GNSS接收器的准确性和可靠性的有希望的方法。

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