A Two-Stage Kalman Filter-Based Carrier Tracking Loop for Weak GNSS Signals

被引:1
|
作者
Cheng, Yan [1 ]
Chang, Qing [1 ]
Wang, Hao [1 ]
Li, Xianxu [2 ]
机构
[1] Beihang Univ, Sch Elect & Informat Engn, Beijing 100191, Peoples R China
[2] State Grid Informat & Telecommun Branch, Beijing 100761, Peoples R China
来源
SENSORS | 2019年 / 19卷 / 06期
基金
中国国家自然科学基金;
关键词
GNSS carrier tracking; high sensitivity; Kalman filter; reduce convergence time;
D O I
10.3390/s19061369
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
For global navigation satellite system receivers, Kalman filter (KF)-based tracking loops show remarkable advantages in terms of tracking sensitivity and robustness compared with conventional tracking loops. However, to improve the tracking sensitivity further, increasing the coherent integration time is necessary, but it is typically limited by the navigation data bit sign transition. Moreover, for standard KF-based tracking receivers, the KF parameters are initialized by the acquired results. However, especially under weak signal conditions, the acquired results have frequency errors that are too large for KF-based tracking to converge rapidly to a steady state. To solve these problems, a two-stage KF-based tracking architecture is proposed to track weaker signals and achieve faster convergence. In the first stage, coarse tracking refines the acquired results and achieves bit synchronization. Then, in the second stage, fine tracking initializes the KF-based tracking by using the coarse tracking results and extends the coherent integration time without the bit sign transition limitation. This architecture not only utilizes the self-tuning technique of the KF to improve the tracking sensitivity, but also adopts the two-stage to reduce the convergence time of the KF-based tracking. Simulation results demonstrate that the proposed method outperforms conventional tracking techniques in terms of tracking sensitivity. Furthermore, the proposed method is compared with the standard KF-based tracking approach, proving that the proposed method converges more rapidly.
引用
收藏
页数:16
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