# A Comprehensive Review of GNSS/INS Integration Techniques for Land and Air Vehicle Applications
## 1 Introduction  
The field of application of autonomously functioning navigation systems is growing every day and includes applications in both civilian and military vehicles. Generally, navigation systems can be divided into inertial navigation systems and satellite navigation systems [1, 2]. Inertial navigation systems determine the current position of an object based on the measurement of the velocity and acceleration of the object. Satellite navigation systems determine the position of an object based on measurements related to other objects, in particular the moments of receiving a synchronized signal from navigation satellites that is used for calculation of the ranges and pseudoranges of navigation satellites [1]. Satellite navigation systems (SNS) based on GNSS receiver subject to the availability of measurements from four navigation satellites can provide an accuracy in determining the position of an object up to 20 m, and accuracy improvement is possible through the use of differential correction methods [3]. However, in cases when the satellite navigation system consists of one navigation GNSS receiver with one antenna, it is impossible to estimate the angular position of the object, which is a disadvantage in cases of application to flying objects. In addition, satellite navigation systems are quite sensitive to external conditions: the presence of obstacles in the form of vegetation and civil buildings, as well as blocking the signals of navigation satellites [3, 4]. Inertial navigation systems (INS) are devices that are completely autonomous from external conditions. They include sensors for measuring velocity and acceleration [5; 6]. Modern inertial navigation systems are platform INS mechanically stabilized using a special platform and strapdown INS (SINS) that are numerically stabilized using special algorithms implemented in an additional computing device of the navigation system [6]. The raw output of the SINS is represented by the angular velocity and the specific force in the associated coordinate system. The position and attitude of the object are determined by integration using initial conditions. Thus, inertial navigation systems make it possible to calculate the position of an object and its attitude. However, inertial navigation systems with all their advantages have a significant drawback, which is that their accuracy decreases over time. The presence of an error in the INS measurements leads to a large error in determination of the object position when integrating the measurement data [6; 7]. Thus, satellite and inertial navigation systems have complementary properties, which has led to a trend of integrating these systems to obtain reliable autonomous navigation systems. Currently, there are many techniques of SNS/INS integration. The simplest and most obvious approach is to use the output data of the SNS on the position and velocity of the object as initial conditions for the INS [7; 8]. This technique is called INS with GPS resetting. However, this technique has several disadvantages. In the most general case, a GNSS with a single antenna provides only an estimation of the position and velocity of an object, which will be used as initial conditions in the INS, while the initial conditions for the angular velocity and attitude will be selected. An inaccurate choice of initial conditions for the attitude for the INS will lead to an unlimited accumulation of errors. In addition, the \"correction\" of the initial conditions for the INS is possible at those moments when the SNS measurements are available. As is known, the operation of the SNS can be disrupted due to intentional signal suppression or unintentional suppression associated with the presence of obstacles. In this regard, using more advanced integration techniques is preferable. Traditionally, more advanced integration techniques are based on the use of the Kalman filter as a tool for estimating INS error based on the use of SNS measurements [8]. This approach is called complementary or GPS-aided INS.