# Design and Validation of a Cascading Vector Tracking Loop in High Dynamic Environments
## 3 Results
### Results Analysis
#### 3.2.2 Signal Analysis Based on the Simulator  
To test the vector tracking mode, the initial user state must be provided, so the data generated by the simulator is first positioned by the scalar mode and then converted to the vector tracking mode (see detail in Table 2). In the high dynamics environment test, the signal is unblocked, and the signal electricity is better than \(-130\) dbm on average. Testing in high dynamics with the signal simulator Spinent GSS8000, the initial position was set to the point of WGS84 coordinates (6378050, \(-7200\), 0), with only the positive speed (5000 m/s) of the Y-axis at the beginning of at the signal initial moment. Only acceleration exists on the Y-axis. Five sets of dynamic data under the accelerations of \(-20\), \(-40\), \(-60\), \(-80\), and \(-100\)g were collected, and the different accelerations were obtained through the variable acceleration movement for 1 s. The motion mode of the carrier in the signal source was set as shown in Table 2 (in the navigation signal simulator, g was set as 10 m/s\({}^{2}\)). In highly dynamic environments, the mature solution is to adopt the second-order frequency locked loop assisted third-order phase locked loop (FLL-PLL). To ensure a good dynamic performance, the noise bandwidth of the FLL-PLL loop was large enough. Although the noise bandwidth was big enough. Table 3 shows that the dynamic range of the vector loop is much higher than that of the FLL-PLL loop, indicating that our vector loop is more robust than the FLL-PLL loop. The initial values must be provided by scalar loops, so the stability of the loop is important when different pre-filters are switched. Thus, the FLL-PLL loop is clearly unfit for this job. Finally, in this case, the increased noise will affect the accuracy of the tracking loop. Table 3 shows that under the same dynamic condition, the carrier phase tracking error of the vector structure is much lower than that of the FLL-PLL loop when the tracking is stable. Figure 10 shows the IQ demodulation graph after the tracking signal of the FLL-PLL loop. The red curve is the amplitude of instant code branch I, and the blue discrete point set is the amplitude of instant code branch Q. The black vertical lines in the figures show the moments when the IQ values converge steadily. Figure 10a shows the IQ demodulations of the \(-20\) g carrier acceleration, and Figure 10b shows the IQ demodulations of the \(-60\) g carrier acceleration. To clarify the influence of different dynamic environments on convergence time, the pictures show the tracking results from the loop switches to stable tracking. In (a), the time consumed by signal tracking from traction to stabilization is approximately 280 ms. However, in (b), the convergence time of signal tracking is approximately 500 ms, indicating that the convergence time of this frequency \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Time (unit: ms)** & **0–1000** & **1000–2000** & **after 2000 ms** \\ \hline Velocity (unit: m/s) & 5000 & vary with the acceleration and acceleration jerk \\ Acceleration (unit: g) & 0 & vary with the acceleration jerk & \(-100/-80/-60/-40/-20\) \\ acceleration jerk(unit: g/s) & 0 & \(-100/-80/-60/-40/-20\) & 0 \\ \hline \hline \end{tabular} (Note 1: The first 7.5 s is scalar tracking, followed by vector tracking), (Note 2: The normal acceleration of gravity g is set to 10 m/s\({}^{2}\)).  
\end{table} 
Table 2: Setting the dynamic changes in experiments.