# Asymmetric Dual-Band Tracking Technique for Optimal Joint Processing of B1I and B1C Signals
## 5 Experiment and Discussion  
In this section, a simulated experiment is carried out based on a software receiver and a GNSS signal simulator which supports the generation of the BDS global signals. In addition to demonstrating the ranging performance advantage of the B1-ADS signal over separate B1I or B1C signals, the experiment is also used to validate the feasibility of the ASYM-DBT method. The software receiver is equipped with a wideband RF unit with a center frequency of 1570 MHz and a bandwidth of 52 MHz that can receive both B1I and B1C signals simultaneously. The entire B1 frequency band is converted to the intermediate frequency of 210 MHz in the RF unit, then is digitized with the sampling rate of 120 MHz, and fed into the PC for subsequent signal reception and processing. The main configuration of the PC platform is as follows: Intel(R) Core(TM) i7-6700K CPU, clocked 4.00 GHz, memory 16.0 GB, NVIDIA GeForce GTX TITAN X GPU. The software receiver can be configured as the following three receiving modes: processing the B1I signal independently, processing the B1C signal independently, and processing the B1-ADS signal with the ASYM-DBT algorithm. The pre-detection integration time \(T\) is set to 10 milliseconds, and the E-L correlation spacing of the B1I and B1C signals are set to one and one-half of the chip length, respectively. The noise bandwidth parameters of code, subcarrier, and carrier tracking loops are set to 0.4, 0.4, and 10 Hz, respectively. In order to evaluate and compare the thermal noise performance of different receiving modes, a specific simulated signal scenario is set in the navigation signal simulator. Two different satellites (PRN 1 and PRN 2) are set to move with the same trajectory and broadcast both B1I and B1C signals simultaneously. Then, two independent channels are arranged in the receiver to process the signals of the two satellites, respectively. Therefore, the differences between measurement outputs of the two channels can be viewed as the thermal noise errors for code, subcarrier, and carrier tracking correspondingly. By adjusting the transmitted power, the standard deviation of the thermal noise errors can be obtained at different C/N\({}_{0}\). Figure 6 depicts the standard deviation of the code, subcarrier, and carrier tracking errors of the ASYM-DBT method while processing the B1-ADS signal. For the convenience of comparison, theoretically derived results are also presented in the same figure. Comparing SLL with DLL, the subcarrier noise error is one order of magnitude smaller than the code noise error. This is the result of the larger Gabor bandwidth of the B1-ADS signal. According to Equation (13), we can combine the subcarrier measurement with the code measurement to form a high-precision and unambiguous pseudorange measurement with centimeter-level accuracy. This high-precision pseudorange measurement is very helpful to rapidly determine the ambiguity of the carrier phase in Precise Point Positioning (PPP) or Real Time Kinematic (RTK) technology [29; 30]. Figure 7 gives the thermal noise performance of DLL and PLL under three receiving modes. It must be noted that the horizontal axis in this figure is still expressed as C/N\({}_{0}\) calculated based on the power of the B1-ADS signal. From Figure 7, it is obvious that the DLL noise performance of the B1-ADS signal is 3 dB better than that of B1I and B1C signals, and the PLL noise performance of the B1-ADS is also better than that of the other two signals. These results confirm that the ASYM-DBT method can effectively combine the B1I and B1C signals to obtain the power gain of the B1-ADS signal. Figure 6: Comparison between the code, subcarrier, and carrier noise errors of B1-ADS. (**a**) Code noise error; (**b**) Subcarrier noise error; (**c**) Carrier noise error. It is worth mentioning that Figure 7 shows that the DLL noise performance of the B1I and B1C signals are almost the same.