# Spoofing Detection Using GNSS/INS/Odometer Coupling for Vehicular Navigation
## 2 GNSS Spoofing Detection Using IMU and Odometer  
INS and their solutions are self-contained and provide high rate measurements. They have good short-term accuracy. However, long term errors grow without bound as the inertial sensor errors accumulate due to intrinsic integration in the navigation algorithm. Navigation solutions based on GNSS need a direct line of sight to at least four satellites, which is not always possible due to satellite signal blockages by tall buildings, trees and tunnel entrances and exits. Taking advantage of the complementary characteristics of these systems, their integration overcomes their individual drawbacks and provides a more accurate and robust navigation solution than neither could achieve. The integrated navigation solution is a continuous high data rate system that provides a full navigation solution (position, velocity and attitude) with improved accuracy in both the short and long term. GNSS prevents the inertial solution from drifting and INS provides continuity in the navigational solution. In the loosely coupled integration case, GNSS navigation solutions and INS mechanization operate independently and provide separate navigation solutions. To improve the solution, the position and/or velocity from GNSS is fed to an optimal estimator, usually a Kalman Filter (KF). The INS solution is also supplied to the filter, which takes the difference between the two and, based upon the error models, estimates the INS errors. In general, two types of feedback approaches are implemented, namely open-loop and closed-loop. In the open-loop configuration, the position, velocity and attitude corrections are performed in the integrated navigation solution (external to the INS) where the estimated errors are subtracted from the INS solution at each iteration. In such a case, the corrected KF states are not fed back to the INS to correct for its drift. In the closed-loop configuration, the error estimates from KF are fed back in order to correct the INS errors. The output of the INS forms the integrated solution. KF position, velocity and attitude estimates are reset to zero after the error estimates are fed back. In the conventional implementation of GNSS/INS, the integration filter runs in prediction mode with the predicted values of the INS during the GNSS outages. In open sky conditions when the receiver antenna has access to Line-Of-Sight (LOS) signals, either the integrated or the unaided GNSS solution can be used. More specifically, under nominal operation conditions the signal and measurement quality of GNSS are high and the KF puts more weight on the GNSS measurements than on prediction. As mentioned previously, the integration of GNSS/INS is beneficial in GNSS outage scenarios. However, in the case of a spoofing attack the reasonable assumption is that the receiver antenna receives spoofed GNSS signals with a high signal strength, resulting in a fake navigation solution. In such a case, GNSS/INS integration under a closed loop scenario with an update rate of a few Hz will not be effective in detecting the spoofing attack. This is due to the fact that in the closed-loop integration, the integrated KF solution's estimated accelerometer and gyroscope errors are fed back to correct the IMU measurements. These errors are applied on every iteration of mechanization, with feedback from KF periodically updating the accelerometer and gyroscope errors. Since the relative dynamics between spoofed GNSS solutions and that of INS are probably not significant for a vehicle during a typically short update interval (a few Hz), the spoofing attack may not be detected. The advantage of the open loop configuration is that in addition to the integrated navigation solution, the raw INS solution can support integrity monitoring and spoofing detection since the inertial based navigation solutions are not affected by the attack. However, due to INS drift, the errors in the INS grow with time to the point that the authenticity verification using this approach is no longer reliable.