Global Navigation Satellite System (GNSS) suffers from the multipath signals reflected by various objects in the vicinity of receivers. A severe multipath environment may enormously hamper the tracking performance, resulting in meter-level pseudorange error. To solve the parameter estimation (angle, polarization, and time delay) problem and enhance multipath mitigation, particularly in the presence of high spatial and temporal correlation between the line-of-sight signal and multipath signals, we propose a novel method based on multi-dimensional processing and multiple sparse Bayesian learning (MSBL). Our method establishes a joint spatial, polarization, and temporal GNSS signal model and utilizes sparsity in the spatial and temporal domains, as well as signal characteristics in the polarization domain, to solve the multi-dimensional processing problem. We then derive an MSBL-based joint estimator of angle, polarization, and time delay for each signal, extending an off-grid estimator for angles and time delays to reduce complexity and improve resolution. Simulation results demonstrate that our approach achieves close-to-optimal performance compared to the Cramér-Rao bound and outperforms other methods, particularly under highly spatially or temporally correlated signals and with multiple multipath signals. These results show that our proposed method has excellent robustness and effectiveness in combating multipath.

中文翻译：

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GNSS 多路径环境中基于多重稀疏贝叶斯学习的联合空间、偏振和时间估计

全球导航卫星系统 (GNSS) 会受到接收器附近各种物体反射的多径信号的影响。严重的多径环境可能会极大地影响跟踪性能，导致米级伪距误差。为了解决参数估计（角度、偏振和时间延迟）问题并增强多径抑制，特别是在视距信号和多径信号之间存在高空间和时间相关性的情况下，我们提出了一种基于多径的新方法。 - 维度处理和多重稀疏贝叶斯学习（MSBL）。我们的方法建立了联合空间、偏振和时间 GNSS 信号模型，并利用空间和时间域的稀疏性以及偏振域的信号特征来解决多维处理问题。然后，我们为每个信号推导基于 MSBL 的角度、偏振和时间延迟联合估计器，扩展角度和时间延迟的离网估计器，以降低复杂性并提高分辨率。仿真结果表明，与 Cramér-Rao 界限相比，我们的方法实现了接近最佳的性能，并且优于其他方法，特别是在高度空间或时间相关的信号以及多个多路径信号的情况下。这些结果表明，我们提出的方法在对抗多径方面具有出色的鲁棒性和有效性。