# A Comprehensive Review of GNSS/INS Integration Techniques for Land and Air Vehicle Applications
## 4 Main Problems of GNSS/INS Integration and Their Solutions
The integration algorithm makes it possible to weight GNSS and INS measurements in different situations. The use of this approach in [56] made it possible to obtain a position determination accuracy of 3.9 m based on the measurements of the Ublox NEO-M8N GNSS receiver and the Pixhawk 2 autopilot with a built-in INS. The idea of measurement classification is also implemented in [76] by using the K-means method, which will divide all incoming raw GNSS receiver data into two main classes: line-of-sight (LOS) or non-line-of-sight (NLOS) signals obtained as a result of multiple \begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline \multirow{2}{*}{**Publication**} & **RMSE/Mean** & **RMSE/Mean** & **RMSE/Mean** & **RMSE/Mean** & **RMSE/Mean** & **Integration Tool,** & **Number of** **Satellites in** & **Number of** **LOS,** \\  & **Position [NED]/** & **Mean Position** & **Velocity** & **Mean** & **Attitude** & **Improving** & **Unavailability of the SNS** \\  & **[LatLon,Alt]** & **3D** & & & & & & **LC, Artificial neural network, Position update & \\  & & & & & & & architecture & \\  & & & & & & utilizing multi-layer & & \\  & & & & & & feed-forward & & \\  & & & & & & NN & & \\ \hline Land Vehicle & & & & & & & & LC, 15-state KE, Error damping & \\  & & & & & & & of INS & \\  & & & & & & & & \\  & & & & & & & & \\  & & & & & & & & \\  & & & & & & & & \\  & & & & & & & & \\ \hline Tightly Coupled & & & & & & & TC, 15-state KE, Robust sequential KE for accurate Vehicular & 0, 60 s \\  & & & & & & & & & \\  & & & & & & & & \\  & & & & & & & & \\  & & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._reflections. Verification of the technique was carried out on an experimental setup based on the GNSS receiver NovAtel ProPak6 and INS with gyro sensor bias \(<0.5^{\circ}\)/h and accelerometer bias 1250 \(\upmu\)g. As a result, the accuracy of positioning in the northern and eastern directions was up to 0.5 m and 1.66 m along the vertical direction. Factor graph optimization is one of the most interesting and effective methods of delivering good positioning accuracy of three common approaches considered for reducing the outliers. However, the need to accumulate and use a set of measurements for the entire period of use of the system in this method will lead to a significant computational load and a possible increase in the response of the system. High positioning accuracy in the presence of outliers is also provided by methods based on neural networks that allow classifying and excluding low-quality measurements. However, the time and computational costs of this operation can significantly exceed similar costs when using a standard EKF with a weighted matrix of coefficients that allows ranking and suppressing low-quality measurements. To provide the integrity of GNSS measurements, the augmentation systems, such as ground-based augmentation system, satellite-based augmentation system and aircraft-based augmentation system, are usually used. Another approach consists of using receiver autonomous integrity monitoring methods (RAIM) [58]. For integrated GNSS/INS systems, one can develop the fault detection and exclusion method by using INS as adding device for fault detection of GNSS receiver. In particular, in [58], the novel integrity monitoring algorithm for the detection of step and ramp errors based on the autonomous integrity monitored extrapolation (AIME) method is represented. In [59], the RTK/GNSS integrated with INS, odometer and Doppler observations is considered. In this work, a new model for computation of protection level that should bound the true position error at a certain probability of risk is presented.