# 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{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt}} \hline \hline \multirow{2}{*}{**Publication**} & **RMSE/Mean Position [1]/L+L,Lon,Alt** & **RMSE/Mean Position 3D** & **RMSE/Mean Velocity [2]/RMSE  
Table 5 provides a comparative description of various approaches to the implementation of integrated INS/GNSS systems in the presence of errors and effects due to the characteristics of the sensors: INS drift, INS sensor alignment error with respect to the GNSS receiver and resulting lever arm effect, etc. Reducing of INS sensor errors is necessary to a large extent since the integrated system actually operates as an INS system when the GNSS signals are unavailable or inaccurate. \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} p{113.8 pt}} \hline \hline \multirow{2}{*}{**Publication**} & **RMSE/Mean** & **RMSE/Mean Position** & **RMSE/Velocity** & **RMSE/Mean** & **RMSE/Mean** & **Mean** & **Technique,** **Integration Tool,** & **Reason of Outliers** \\  & **[Lat,Lon,Alt]** & 3D & [p\({}_{\mathrm{w}_{\mathrm{w}}}\),\({}_{\mathrm{p}}\),\({}_{\mathrm{w}}\)] & **Velocity** & **Velocity** & **3D** & **[P,R,Y]** & **Method** \\ \hline Fault Detection of Resilient Navigation System Based on GNSS Pseudorange Measurement [74] & RMSE Lat = 4.7 m & - & \(\mathrm{p}_{\mathrm{N}}\) = 0.08 m/s \(\mathrm{p}_{\mathrm{F}}\) = 0.18 m/s \(\mathrm{p}_{\mathrm{F}}\) = 0.18 m/s \(\mathrm{p}_{\mathrm{D}}\) = 0.14 m/s & - & - & Factor graph method, Fault detection and isolation method & Vibration interference, multipath effect \\ \hline Multipath/NLOS Detection Based on K-Means Clustering for GNSS/INS Tightly Coupled Areas [76] & RMSE Lat = 1.66 m & - & - & - & - & - & \begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \\ \end{tabular}  & Multipath/NLOS Algorithm based on K-means [76] & & & & & & \\ \hline Tightly Coupled Areas [76] & & RMSE & & & & & & & \\ \hline Tightly Coupled  GNSS/INS Integration Via Factor Graph  
Mainly, the approaches based on the exact construction of models and estimation of stochastic errors of INS are used in the considered works to deal with these errors. In particular, a method based on the use of an autoregressive model instead of the Gauss-Markov model, which is often used in INS systems [29; 31; 78], is proposed. In this case, the measurement error model is a linear combination of previous measurements. In [29; 31; 78], an autoregressive INS error model was used in conjunction with additional noise filtering based on the wavelet analysis technique. A NovAtel OEM4 GNSS receiver and Honeywell HG1700 IMU INS were used in this research that made it possible to obtain an average positioning accuracy of up to 1.5 m in the absence of a GNSS signal from 70 to 180 s. In [78], an average positioning accuracy of 4.12 m was achieved using the 3 DM-GX3-25 INS and the Ublox LEA-5X GNSS receiver. The authors of [30; 78; 79] proposed a method based on the Allan variance technique that makes it possible to consider long-term noise in the measurement error model. In [30], the error covariance of INS sensors was estimated based on the Allan variance analysis for INS MI-GA3350. The resulting sensor error model was further used in the Kalman filter with a state vector of dimension 24. In the course of verification, the authors obtained an average positioning error up to 4 m. In [78], an average positioning accuracy of 2.71 m was achieved using the 3 DM-GX3-25 INS and the Ublox LEA-5X GNSS receiver in case of GNSS signal outage for the period of 60 sec. Stochastic error modelling using the Allan variance technique and the evaluation of error covariance of INS sensors MP-POS 830 (Wuhan MP Space-Time technology Company) was performed in [79].