# A Comprehensive Review of GNSS/INS Integration Techniques for Land and Air Vehicle Applications
## 5 Conclusions and Future Directions  
In this paper, an analysis of the current state in the field of available approaches of INS/GNSS integration for determination of the position of mobile objects representing land or air vehicles is carried out. The main integration approaches are considered. Loosely coupled and tightly coupled integration systems are of the greatest interest in this review. The main integration techniques based on the extended Kalman filter and its modifications, for example, unscented Kalman filter, are considered. However, in this case, for loosely coupled and tightly coupled systems, it is necessary to consider all the static and dynamic errors of the INS sensors in the state vector of the Kalman filter, the dimension of which can reach 21 elements. For these errors, accordingly, sensor error models should be introduced, which greatly complicates and burdens the algorithm. In this regard, for more practical benefit, it is convenient to introduce simplifications. Some authors have proposed ideas for separating the processes of estimating the attitude and position of an object with separate algorithms of estimation. By introducing magnetic sensors into an integrated system, they obtain an AHRS system (attitude heading reference system) that can estimate the attitude of the object and the gyro sensor error by using a certain algorithm, while the position and velocity of the object, as well as the accelerometer bias, are determined in another filter. This approach to integration can be of great practical use due to its relative simplicity. The main factors affecting the accuracy and functionality of the system, in particular the temporary unavailability of GNSS signals, outliers and integrity violation and INS errors, are identified. According to the results of the analysis, the use of neural networks for simulation of GNSS signals that are not in the field of view provides a relatively good result in terms of accuracy when used in integrated GNSS/INS systems. However, the time and computational costs for their implementation can significantly exceed similar costs when using the standard EKF. In this regard, in many cases, the use of approaches to integration based on the Kalman filter is most preferable. A more reliable approach to solve the problem of temporary unavailability of GNSS measurements is the use of tightly coupled \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline \multirow{2}{*}{**Publication**} & **RMSE/Mean** & **RMSE/Mean** & **RMSE/Mean** & **RMSE/Mean** & **RMSE/Mean** & **Integration Tool,** & **Resolution/Outliers** \\  & **Position [**1**ED]/** & **Mean Position** & **Velocity** & **Mean** & **RMSE/Mean** & **ICR, KF, Allan variance analysis & **Accelerometer** \\  & **[**Lat,Lon,All**]** & **3D** & \(\text{I}_{\text{N}_{\text{N}}},\text{I}_{\text{E}_{\text{T}}},\text{I}_{ \text{D}1}\) & **Velocity** & **3D** & **Atitude** & **LON,KF, Allan variance analysis & **Accelerometer** \\  & & & & & & & \\ \hline \hline \end{tabular}  
* Using Allan variance to improve stochastic modeling for accurate GNSS/INS integrated navigation [99]  
\end{table} 
Table 5: _Cont._integration systems, as well as GNSS receivers that work with several navigation systems: GPS, Beidou, GLONASS. Machine learning and neural network technologies are also used in the development of adaptive integration techniques in the presence of outliers. However, for the reason described above, their use may not be beneficial in terms of computing resources. An EKF with a weighted coefficient matrix to rank and suppress poor measurements can solve the problem with outliers just as well. INS sensors have many static and dynamic errors. Reducing INS sensor errors is necessary to a large extent as the integrated system actually operates as an INS system in the periods of absence of GNSS signals or their quality deterioration.