发表机构
Iran University of Science and Technology（伊朗理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对城市环境中GNSS信号脆弱问题，提出GLRT辅助的离网格SBL方法，用于LEO机会信号导航，在多径和非高斯噪声下实现高精度延迟-多普勒估计，显著降低延迟和位置误差。
AI 中文摘要
全球导航卫星系统（GNSS）信号在密集城市环境中的脆弱性和不可用性，推动了利用低地球轨道（LEO）机会信号（SoOP）进行定位、导航和授时（PNT）的研究。然而，多径和非理想噪声会显著降低延迟-多普勒估计的精度，进而影响导航精度。本文提出了一种混合广义似然比检验（GLRT）与离网格稀疏贝叶斯学习（SBL）框架，用于多径和非高斯、时间相关噪声条件下的LEO-SoOP导航。首先，开发了基于GLRT的框架，证明其在理想高斯噪声下具有可靠的检测性能，但在重尾或有色噪声以及未解析多径情况下估计性能会下降。接着，引入离网格SBL框架，用于高分辨率分离和估计视距（LOS）延迟-多普勒参数。估计的LOS参数随后被跟踪并集成到扩展卡尔曼滤波器（EKF）中，以获得导航解。仿真结果表明，在城市环境中，与基于GLRT的估计相比，所提方法将延迟RMSE降低了高达77%，位置RMSE降低了88.5%。这些结果展示了所提框架在鲁棒和精确的LEO-SoOP导航方面的潜力。
英文摘要
The vulnerability and unavailability of global navigation satellite system (GNSS) signals in dense urban environments have motivated the use of low earth orbit (LEO) signals of opportunity (SoOP) for positioning, navigation, and timing (PNT). However, multipath and non-ideal noise can significantly degrade delay--Doppler estimation and, consequently, navigation accuracy. This paper proposes a hybrid generalized likelihood ratio test (GLRT) and off-grid sparse Bayesian learning (SBL) framework for LEO-SoOP navigation under multipath and non-Gaussian, temporally correlated noise. First, a GLRT-based framework is developed, demonstrating reliable detection under ideal Gaussian noise but degraded estimation under heavy-tailed or colored noise and unresolved multipath. Next, an off-grid SBL framework is introduced for high-resolution separation and estimation of line-of-sight (LOS) delay--Doppler parameters. The estimated LOS parameters are subsequently tracked and incorporated into an extended Kalman filter (EKF) to obtain the navigation solution. Simulation results demonstrate that, in urban environments, the proposed method reduces delay RMSE by up to 77\% and position RMSE by 88.5\% compared with GLRT-based estimation. These results demonstrate the potential of the proposed framework for robust and accurate LEO-SoOP navigation.
Comments11 pages, 5 figures
