# A Comprehensive Review of GNSS/INS Integration Techniques for Land and Air Vehicle Applications
## 1 Introduction
In particular, it is assumed that, when a ground vehicle moves, it does not slip off the trajectory and does not bounce, that is, the motion occurs in a constant direction; the velocity of the object in the direction perpendicular to the direction of its motion is equal to zero. Taking into account the considered kinematic limitations and features of the vehicle dynamics will improve its positioning accuracy. If we do not consider the proposed restrictions on maintaining the direction of motion, a lever arm effect arises due to the fact that the INS and GNSS are installed in different places on the vehicle, which leads to a difference in the velocity measurements of the INS and GNSS [6]. These limitations can be considered in the Kalman filter to obtain better positioning accuracy. As shown in [43], the constrained Kalman filter provides better accuracy than the traditional filter. In [44], the error from the lever arm effect is included in the INS error vector for estimation in the Kalman filter. Further, in [45; 46], the authors continued their work and assessed the influence of the lever arm for the integrated SNS/INS system installed on a car driving along a given trajectory. The research results showed that the estimation of the effect of the lever arm and the angular position of the object are 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 to estimate its influence. In [48], it is proposed to use \"virtual\" measurements of the lever arm for use in the Kalman filter, which estimates the error of the INS. The development of integrated INS/GNSS systems for air transport, in particular unmanned aerial vehicles, also uses the main integration tools, such as the Kalman filter. The main difference from ground transport is that the UAV has more complex dynamics and, accordingly, a trajectory. In the works of the authors, both loosely coupled and tightly coupled integration schemes are used. In particular, in [49], the implementation of a loosely coupled navigation system for an agricultural UAV is considered. In [50], both a loosely coupled and a tightly coupled system for a helicopter-type UAV under various conditions imposed on the flight path, as well as under various conditions of availability of GNSS signals, is considered. The problem of development of an integrated system for helicopter-type UAVs in the presence of a magnetic sensor in addition to the integrated INS/SNS system is considered in [51]. In [52], the problem of creating a tightly coupled system for an UAV copter based on commercially available INS and GNSS navigation sensors with multiple antennas is considered. The main advantage of this work consists of using double differences instead of single difference. It helps to remove the clock errors. Although [52] is out of the scope of this article, devoted to the use of single-antenna GNSS receivers, it deserves attention as one of the interesting engineering approaches to the design of integrated systems. In [53], a tightly coupled system based on a broadband GNSS receiver and an INS is considered using the factor graph optimization method as an integration tool, allowing you to reduce the problem of finding a navigation solution to a non-linear least squares method, which improves the accuracy of the UAV navigation system in indoor conditions, with a high probability of the influence of the multipath effect in the absence of direct radio visibility. The papers [54; 55] consider budget integrated SNS/INS systems for UAVs based on cheap commercially available INS with a low accuracy included strapdown system. Works [56; 57] are devoted to improving the integrated INS/SNS system by developing an adaptive Kalman filter that is improved using machine learning to classify GNSS errors. For air transport applications, where safety is a concern, it is important to improve the reliability of measurements conditioned by the integrity of the measurements.