# 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 multipath effect of the GNSS signal due to the reflection of the direct signal from volumetric objects is the most common reason in the reviewed studies. The ionospheric and tropospheric effects due to signal delays in the ionosphere and troposphere are less common reasons. Several approaches have been explored by authors to reduce the effect of outliers. It is proposed to use a robust Kalman filter with an equivalent weighted matrix in the measurement model [6], which makes it possible to suppress the effect of measurements with large errors. The use of a robust Kalman filter for an integrated system with INS NovAtel IMU-FSAS with a frequency of 100 Hz, which has a gyro sensor bias \(<0.75^{\circ}\)/h and an accelerometer bias \(1.0\)\(\upmu\)g, and a GPS receiver with a frequency of 1 Hz, makes it possible to achieve a positioning accuracy in the northern direction of up to 2.2 m. Simulation modeling of integrated system with a positioning accuracy of the GNSS receiver up to 5 m with non-specific fluctuation of the carrier frequency contour, causing the presence of non specific low-quality GNSS measurements, was carried out in [28]. A method for reducing the influence of poor-quality measurements using a weighted matrix of coefficients (gain matrix) is presented in [72]. This method made it possible to obtain a positioning accuracy of up to 5 m with a loosely coupled and tightly coupled integration scheme based on the use of a GNSS receiver Novatel OEMV-3 and INS STIM-300 with gyro sensor bias \(<250^{\circ}/\mathrm{h}\) and accelerometer bias 0.75 \(\upmu\)g.  
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Factor graph optimization (FGO) method is gaining some popularity to ensure the integration of GNSS and INS measurements. Previously, this method was mostly used in robotics and is similar to the well-known SLAM approach in robotics. This method is presented as a probabilistic graphical model containing various nodes associated with system states and factors representing measurements. Unlike traditional EKF-based integration, the FGO method takes into account both the entire set of measurements and system updates to optimize the entire state vector. After all measurements and states are encoded in the factorial graph, the integration problem is iteratively solved through optimization using the Gauss-Newton method. FGO also processes the delayed measurements as they are simply additional sources of factors that are added to the factor graph as they arrive [73]. In [53], this technique was applied to determine the position of an object inside a premises where outliers appear due to reflection. According to the results of the work, the obtained positioning accuracy of the object in the plane of motion is 0.21 m, and 0.09 m in the vertical direction when using the high-precision SNS system LinkTrack UWB and INS Xsens Mti-10. The authors of [74] developed an integration system with the detection of low-quality pseudorange measurements based on the factor graph and tested it on a simulation model. The authors of [73; 75] also applied the factor graph optimization for tightly coupled integration of INS/GNSS measurements, which made it possible to obtain an average position error of up to 4 m based on measurements of the Xsens Ti-10 INS and the Ublox M8T GNSS receiver working with both the GPS system and the Beidou system. In the process of research, it was also determined that scaling measurements that are not included in the direct field of view of the GNSS receiver will reduce the effect of outliers. Machine learning and neural network technologies are also used in the development of adaptive integration techniques in the presence outliers. For example, in [56], a classification model based on machine learning that predicts various changes in the properties of the GNSS signal depending on the reason of outliers was used to develop an integration method based on the Kalman filter.