## 4 Experiments and Discussion
### Performance of Variable Circular Correlation Time  
Variable circular correlation times are adopted for the coarse signal acquisition of all the datasets in the experiments to explore the relationship between circular correlation time and time cost of signal acquisition, as well as acquisition effectiveness. For all the datasets, the number of satellites acquired without/with the resampling strategy is shown in Figure 12 when circular correlation time was varying from 10 ms to 20 ms. The experimental results are exhibited as the comparative red and blue plots to illustrate the effects of the resampling strategy. There are several cases, marked as green circles in Figure 12c,d, in which the number of satellites acquired with the resampling strategy was fewer than that with the conventional acquisition algorithm. However, there were still enough acquired satellites for navigation solution in these cases. Except that, the majority of experimental results with the resampling strategy were the same with that of the conventional one. This means that acquisition effectiveness of the resampling strategy can primarily reach that of the conventional acquisition algorithm, which cost about 10 times computation and time of the resampling strategy. Further, the number of satellites acquired without/with the resampling strategy nearly remained unchanged when circular correlation time was increasing from 12 ms to 20 ms. This indicates that there is potential to shorten the circular correlation time so as to reduce computation and time cost, without obvious loss of acquisition performance. Whereas, too short circular correlation time reasonably leads to the incomplete acquisition of visible satellites, as cyan circles in Figure 12a,c. Figure 11: Computation of signal acquisition without/with the resampling strategy for all experimental datasets: (**a**) Multiplication computation in linear axis; (**b**) Multiplication computation in log axis; (**c**) Summation computation in linear axis; (**d**) Summation computation in log axis. The time cost performance of variable circular correlation time on all experimental datasets is presented in Figure 13, when acquiring without/with the resampling strategy. Noticeably, when circular correlation time was varying from 10 ms to 20 ms, the time cost of signal acquisition with the resampling strategy (red bars) were much less than that of the conventional algorithm (blue bars), nearly decreased by 60-90% for Datasets 1, 2, and 3, while 5-35% for Dataset 4. This is attributed to the fact that the bandpass filter of the resampling strategy is not suitable for the signal with negative intermediate frequency in Dataset 4. The experimental results in Figure 13 illustrate the capability of the resampling strategy to decrease the time cost of signal acquisition. In addition, the blue and red plots in Figure 13 reflected the changes of time cost when the circular correlation time was varying. The time cost of signal acquisition without the resampling strategy seemed to increase plainly. Nevertheless, by linearly fitting these values, the time cost exhibited an increase of 3.6%, 3.6%, 3.8%, and 4.7% per millisecond for the 4 datasets, respectively, when the circular correlation time was varying from 10 ms to 20 ms. By contrast, the time cost for the resampling strategy looked stable, despite the existence of some outliers. Actually, the time cost of signal acquisition increased by 2.7%, 2.7%, 5.7%, and 2.7% per millisecond for the 4 datasets, respectively. These experimental results indicate that a relatively shorter circular correlation time is beneficial for signal acquisition with both the proposed resampling strategy and the conventional acquisition algorithm, leading to less computation and time cost without obvious loss of acquisition performance. Figure 12: Number of satellites acquired without/with the resampling strategy for variable circular correlation time.