# An Efficient Ultra-Tight GPS/RISS Integrated System for Challenging Navigation Environments
## 5 Results and Analysis  
The selected trajectory is a simulation of a land vehicle driving at a constant speed of 36 km/h. The total trajectory length is 15 min. However, data logging started at five minutes into the trajectory time. The first few minutes are skipped to let the logging equipment ease into a steady state while receiving signals from the simulation hardware. Thus, the last ten minutes of the trajectory data are processed. Nevertheless, only about nine-and-a-half minutes are shown in the figures since the focus here is on the tracking and navigation stages where the first 36 s are used to extract the navigation message before the receiver can generate a position solution.  
Figure 11: Data Simulation Tool Flow Diagram.  
Figure 10: A photo of the Equipment (Outside of the Room).  
The odometer data rate was set to 100 Hz with a correlation time of one hour for the scale factor (SF) error. The odometer white noise standard deviation is 0.01 m/s. settings for the inertial sensors' errors are given in Table 1.  
GPS signal jamming is one of the most critical error sources affecting navigation solutions across the world. Moreover, recent research demonstrates that GNSS/INS integration is now more important than ever due to the known immunity of INS against most of GNSS error sources including signal interference and jamming. Thus, it is important to examine the performance of the proposed method under jamming conditions. A low-power Continuous Wave (CW) jamming signal is selected. In this scenario, a real jamming signal is broadcast inside the anechoic chamber room. The spectrum of this signal is shown in Figure 12. This was obtained in the lab using a spectrum analyzer. From in lab analysis, and compared to a reference signal, it was found that the low power signal is about one Microwatt and the high-power signal is about one Milliwatt, millions of times stronger than a typical GPS signal. As can be seen Figure 12, the central frequency is around 1.5754 GHz, which is at the central frequency of the GPS system. A CW signal should theoretically have no bandwidth but since the system is nonideal, it is showing a bandwidth of 2.4 kHz. The jammer was turned on for 90 s, commencing at about three minutes from the beginning of the trajectory.  
To make the discussion more valid, we here compare the performance of the introduced system versus a scalar tracking system (ST) and vector tracking system (VT), also implemented on the side of this work. Since the jamming signal frequency was around the GPS central frequency, most of the satellites in view were affected. Carrier frequencies of two sample satellites are shown in Figures 13 and 14, respectively. The ST consequently lost track of some of the satellites leading to large position and velocity errors. The VT system was affected only when the jammer was on, but it immediately recovered tracking of all satellites signals once the jammer was off. This can be seen in the last two figures. The UT system experienced small errors and maintained the ability to track satellites signal even during the jamming period. The position and velocity error results for the three systems are summarized in Table 2. The ST has dramatic errors in all position components. The position errors for the VT system are, however, still bound to a certain extent, even during the jamming scenario. The UT system  
\begin{table}
\begin{tabular}{c c c} \hline \hline Gyroscope & Bias instability \(<\) 10 [deg/h] & Noise (ARW) 1 (deg/sqrt(hr)) \\ \hline Accelerometers & Bias instability 1 [deg/h] & Noise (VRW) 0.05 (m/s/sqrt(hr)) \\ \hline \hline \end{tabular}
\end{table}
Table 1: IMU sensors specifications.  
Figure 12: The spectrum of a Low Power CW Jamming Signal.  
shows a much higher resistance to the jamming effect, maintaining an acceptable solution throughout the trajectory.  
Figure 14: Carrier Frequency PRN13.  
Figure 13: Carrier Frequency PRN6.