# A Review of High-Sensitivity Tracking Techniques for Satellite Navigation Signals
## 3 Results  
This section presents comparative experiments to assess the performance of different tracking techniques under varying sensitivity conditions. The tracking techniques evaluated include Salor Tracking, VT, the Conventional Approach, and DPE. The primary evaluation metrics are positioning error and tracking error. To simulate varying signal strength environments, the experiments modulated the carrier-to-noise density ratio CNR of the received signals. The CNR range was set from 45 dB-Hz to 24 dB-Hz with a step size of 1 dB. To ensure consistency across different tracking methods, all techniques were configured with identical parameters: a loop bandwidth of 50 Hz and a fixed integration time of 1 ms. The experimental environment was static, with the receiver held stationary to isolate the effects of satellite dynamics, thereby eliminating interference from receiver motion. The signal source employed a multi-satellite simulation with a fixed number of six satellites, broadcasting GPS L1 C/A signals. The positioning error results are shown in Figure 13. The figure provides a detailed illustration of the positioning error variations in three coordinate directions (X, Y, Z) for Salor Tracking, VT, the Conventional Approach, and DPE. It can be observed from the figure that there are significant differences in the error magnitude and fluctuation among different tracking methods across various coordinate axes. Specifically, Salor Tracking and VT demonstrate superior positioning accuracy compared to the Conventional Approach and DPE, particularly under low-CNR conditions, where their advantage is more pronounced. However, in terms of stability, VT and DPE exhibit better performance with smaller error fluctuations, while Salor Tracking and the Conventional Approach show relatively larger error variations. These results are consistent with the theoretical analysis presented earlier, indicating that vector tracking methods (such as VT and DPE) have certain advantages in terms of accuracy and stability in complex environments. To further evaluate the sensitivity characteristics of different tracking methods, the tracking error results are presented in Figure 14. It can be clearly observed from the figure that the error of all tracking methods increases gradually with the decrease in the CNR. Under high CNR conditions, Salor Tracking and VT exhibit similar performance with comparable error levels. However, as CNR decreases, VT outperforms Salor Tracking and demonstrates superior stability in low-signal-to-noise-ratio environments. In contrast, the Conventional Approach and DPE show more significant error fluctuations, with error values generally higher than those of Salor Tracking and VT. Nevertheless, the Conventional Approach and DPE exhibit similar stability to VT, maintaining relatively stable error levels. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Method** & **Advantages** & **Representative References** \\ \hline Relaxed DPE & Reduces computational complexity via precomputed correlation functions. [PERSON] (2017) [PERSON] (2017) [PERSON] (2017) [53] \\ \hline Dual-Mode DPE & Utilizes multi-constellation signals to enhance robustness in challenging environments. & [PERSON] (2025) Jia (2025) Jia (2025) [143] \\ \hline NLOS-Aided DPE & Treats NLOS signals as useful reflections to improve positioning accuracy. & [PERSON] (2016) [PERSON] (2016) [144] \\ \hline Machine Learning-Assisted DPE & Integrates random forest regression to mitigate multipath/NLOS errors. [PERSON] (2024) [PERSON] (2024) [146] \\ \hline Single-Difference Code-Based DPE & Reduces satellite clock and atmospheric errors to enhance DPE accuracy. [PERSON] (2024) [PERSON] (2024) [PERSON] (2024) [147] \\ \hline \hline \end{tabular}
\end{table} 
Table 9: _Cont._These results validate the previous analysis: closed-loop tracking methods have higher sensitivity than open-loop tracking methods, and estimation-based methods exhibit higher sensitivity under low-CNR conditions.