# A Comprehensive Review of GNSS/INS Integration Techniques for Land and Air Vehicle Applications
## 4 Main Problems of GNSS/INS Integration and Their Solutions
\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
**Publication** & **RMSE/Mean Position [NED]/[LatLon,All]** & **RMSE/Mean Position 3D** & **RMSE/Mean Velocity [Inv, vz, vz, vz] & **RMSE/Mean Velocity 3D** & **Integration Technique, Attitude [LPR,Y1]** & **Integration Technique, Integration Tool, Improving Method** & **Reason of Outliers** \\ \hline Accurate INS/DCPS positioning using INS autoregressive (AR) modeling of inertial sensor errors [29] & Mean & 1.16 m & - & - & LC, Kalman filter, AR modeling of & Accelerometer and gyro sensor biases \\ \hline Performance Improvement of GPS/INS Integrated System Using Allan Variance Analysis & Mean & - & - & - & LC, 24-state KF, Allan variance analysis method & Random bias, random walk \\ \hline Combined Algorithm of Improving INS Error Modeling and Sensor Measurement Loss/GPS Navigation, GPS Solutions & Mean & - & - & - & LC, Kalman filter, AR modeling of INS errors, wavelet de-noosing & Accelerometer and gyro sensor biases \\ \hline \hline \end{tabular}
\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}} \hline \hline
**Publication** & **RMSE/Mean Position [LED]/[LatLon,All]** & **RMSE/Mean Velocity 3D** & **RMSE/Mean Velocity 3D** & **RMSE/Mean Attitude [LPR,Y1]** & **Integration Tool, Improving Method** & **Reason of Outliers** \\ \hline Accurate INS/DCPS positioning using INS autoregressive (AR) modeling of inertial sensor errors [29] & Mean & 1.16 m & - & - & LC, Kalman filter, AR modeling of & Accelerometer and gyro sensor biases \\ \hline Performance Improvement of GPS/INS Integrated System Using Allan Variance Analysis & Mean & - & - & - & LC, 24-state KF, Allan variance analysis method & Random bias, random walk \\ \hline Combined Algorithm of Improving INS Error Modeling and Sensor Measurement Loss/GPS Navigation, GPS Solutions & - & Mean & - & - & LC, Kalman filter, AR modeling of & Accelerometer and gyro sensor biases \\ \hline \hline \end{tabular}
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**A hybrid error modeling for MEMS IMU in integrated GPS/INS navigation system [32]** & - & RMSE & - & - & LC, 9-state KF, Nonlinear error techniques (fast orthogonal search) & Accelerometer and gyro sensor noises \\ \hline \hline \end{tabular}
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**A Novel KGP Algorithm for Improving INS/GPS Integrated Positioning Accuracy [36]** & - & RMSE & - & - & - & LC, 15-state KF, linear error techniques (fast orthogonal search) & Accelerometer and gyro sensor noises \\ \hline Land Vehicle Navigation System Based on the Integration of Strap-Down INS and GPS [39] & RMSE & & RMSE & & LC, 15-state KF, linear error damping \(v_{E}=\)0.593 m/s & - & - & LC, 18-state KF, linear error damping of INS & Accelerometer and gyro sensor noises \\ \hline GNSS antenna lever arm compensation inertial navigation of UAVs [47] & - & - & - & - & RMSE \(P=2.5^{\circ}\) & LC, 20-state KF, linear error \(R=2.3^{\circ}\) & Accelerometer and gyro sensor biases \\ \hline GNSS/INS Fusion Virtual Lever Arm Measurements [48] & RMSE & & & & & & LC, 18-state KF, linear error measurements & Lever arm effect, accelerometer and gyro sensor biases \\ \hline A Comparison between different error of MEMS Applied to GPS/INS Integrated Systems [78] & - & Mean & - & - & - & LC, KF, Autoregressive modeling of INS errors & Accelerometer and gyro sensor bias \\ \hline \hline \end{tabular}
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Table 5: Comparative characteristics of various approaches to the implementation of integrated INS/GNSS systems in the presence of INS sensor errors. This manuscript mainly addresses the issues of positioning of land and air vehicles. There is another important problem for autonomous driving vehicles that is not covered here. This problem is tracking of moving autonomous vehicles.