This article presents a two-step stochastic hybrid estimation (TS-SHE) algorithm for robust and accurate carrier phase tracking of global navigation satellite system (GNSS) signals in urban environments with significant power degradation and fluctuation. The proposed algorithm adaptively combines a bank of parallel Kalman filters (KFs) with different dynamic state models to cope with the nonstationarity of GNSS signals. Different from conventional approaches, including the phase lock loop (PLL), the KF, and the interacting multiple-model (IMM) method, which strongly rely on the a priori fixed signal model to ensure good tracking performance, we develop a novel stochastic filter transition strategy utilizing the information from reliable signal condition evaluation to overcome the performance degradation caused by the model mismatch in urban environments. Therein, for the first time, we use the prior information of signal fading conditions for more accurate combined weighting of all component filters. Specifically, to obtain the prior information, GNSS data are sequentially buffered and analyzed in the first step. Then, in the second step, optimized filter weights are generated, and the overall combined carrier phase is estimated. We analyze the theoretical performance of the new algorithm and show the effects of different design parameters on the performance. The primary advantages of the proposed algorithm include: 1) the ability to rapidly recover carrier phase observation when blocked signals are reacquired and 2) high accuracy in carrier phase estimation of GNSS signals corrupted by strong multipath fading. Simulation and real data experiment results show the enhanced robustness and improved accuracy of the proposed TS-SHE algorithm compared with conventional carrier phase tracking methods.

中文翻译：

---

城市环境中 GNSS 载波相位跟踪的两步随机混合估计

本文提出了一种两步随机混合估计 (TS-SHE) 算法，用于在具有显着功率衰减和波动的城市环境中对全球导航卫星系统 (GNSS) 信号进行稳健而准确的载波相位跟踪。所提出的算法自适应地将一组并行卡尔曼滤波器 (KF) 与不同的动态模型相结合，以应对 GNSS 信号的非平稳性。不同于传统方法，包括锁相环 (PLL)、KF 和交互多模型 (IMM) 方法，它们强烈依赖于先验固定信号模型以确保良好的跟踪性能，我们开发了一种新颖的随机滤波器转换策略，利用来自可靠信号条件评估的信息来克服模型在城市环境中不匹配导致的性能下降。其中，我们首次使用信号衰落条件的先验信息对所有分量滤波器进行更准确的组合加权。具体来说，为了获得先验信息，首先对 GNSS 数据进行顺序缓冲和分析。然后，在第二步中，生成优化的滤波器权重，并估计整体组合载波相位。我们分析了新算法的理论性能，并展示了不同设计参数对性能的影响。所提出算法的主要优点包括：1) 在重新获取阻塞信号时快速恢复载波相位观测的能力； 2) 对被强多径衰落破坏的 GNSS 信号的载波相位估计精度高。仿真和真实数据实验结果表明，与传统的载波相位跟踪方法相比，所提出的TS-SHE算法具有更强的鲁棒性和更高的精度。