# A Comprehensive Review of GNSS/INS Integration Techniques for Land and Air Vehicle Applications
## 4 Main Problems of GNSS/INS Integration and Their Solutions
Further, integrating the measurements of the INS with those of a GNSS receiver developed on the basis of the NovAtel receiver OEMV-3 and HX-B5581A (Harxon) in the Kalman filter, an average positioning accuracy of 4.23 was obtained in the absence of a GNSS signal for 60 s.  
The next three works are devoted tomethods for compensating errors in INS sensors. The paper [32] proposes an INS error model based on two tools. One of the tools is the fast orthogonal search (FOS)--a technique for nonlinear errors modeling. The second one is the support vector machine (SVM) method to estimate and eliminate the errors of the INS gyro sensor without the need to use the output parameters of the Kalman filter to estimate the error of the INS in the periods of unavailability of GNSS measurements. An approach for compensating INS errors based on the integration of the Kalman filter and Gradient Boosting Decision Tree is proposed in [36] for the periods of absence of GNSS signals. In [39], a method for compensating of INS errors by introducing damping coefficients is proposed. Many works are devoted to the study of the lever arm effect on the quality of measurements of an integrated system. The lever arm represents the position of the GPS antenna in relation to the INS system. The presence of this relative distance can lead to the fact that the velocity of the object determined on the basis of GNSS measurements will differ from the velocity determined on the basis of the INS when the direction of motion of the object changes in the plane of its motion. The resulting error is called the lever arm effect. The error from the lever arm effect on large vehicles can significantly exceed the GNSS positioning error. To consider the influence of the lever arm effect, it can be taken into account when compiling the GNSS measurement vector. However, in practice, this distance is much more difficult to measure and consider in the integration algorithm, which can lead to error accumulation after a while. In this regard, the researchers carried out the work on assessing the influence of the lever arm effect and proposed to take it into account in the Kalman filter to estimate the INS error. In particular, the error due to the lever arm effect was included in the INS error vector for estimation in the Kalman filter in [44]. Further, in [45; 46], the authors continued their work and assessed the influence of the lever arm effect for the integrated GNSS/SNS system installed on a car driving along a given trajectory. The results of the research showed that the lever arm effect is quite sensitive to the trajectory of the object and the nature of its motion. In [47], a new way of specifying the lever arm in the form of spherical coordinates in the Kalman filter was proposed. In [48], it was proposed to use \"virtual\" measurements of the lever arm for use in the Kalman filter that estimates the INS error. From the analysis of publications on the influence of INS errors on the accuracy of the integrated INS/GNSS system, it is obvious that a long absence of data from the SNS can lead to a significant decrease in the accuracy of the integrated INS/GNSS system even when using the most accurate models of INS errors. The proposed approaches to the elimination of INS errors based on the exact construction of models and estimation of INS stochastic errors, for example, the autoregressive model or the Allan variance, provide almost the same accuracy of the integrated system. The choice between one or another approach is determined by the computational capabilities of the designed system and the characteristics of the INS sensors. As to the lever arm effect, it makes sense to take it into account when used on objects with a complex trajectory.