# A Comprehensive Review of GNSS/INS Integration Techniques for Land and Air Vehicle Applications
## 1 Introduction
The first technique based on the GPS-aided INS approach is called loosely coupled integration technique. In a loosely coupled integration system, when obtaining measurements of the SNS in the form of the velocity and position of the object, they are used to construct a complementary vector of measurements of the Kalman filter, which estimates the error of the INS. In the absence of SNS measurements, the estimated error of the INS is equal to the predicted error of the INS in the Kalman filter. The resulting INS error estimate is then subtracted from the INS measurements, thereby providing the output parameter of the integrated system. In this case, the complementary measurement vector is the difference between the measurements of the position of the object determined by the SNS and the INS [7; 8; 9; 10; 11; 12]. That is, the SNS has already implemented a solution to the navigation problem of determining the position and velocity of an object in the presence of measurements from four satellites. This integration scheme is the simplest but has obvious drawbacks. Namely, if signals from four navigation satellites are unavailable, the SNS will not determine the position of the object and then the integrated system works as INS, which, as is known, accumulates an error over time [7]. Despite this, the loosely coupled scheme is the most commonly implemented integration scheme, since, in fact, separate data of INS and SNS are used to \"correct\" the error of the integrated SNS/INS system. This allows the use of any commercially available SNS and INS. Currently, some modifications of the loosely coupled integration system are known. For example, in [13], it is proposed to introduce a model of fictitious navigation satellites having a certain position in space. It is proposed to use satellites that are not currently in sight, as well as artificial (fictitious) navigation satellites. In [14], a quasi tightly coupled integration technique based on a loosely coupled scheme is proposed. In this case, the Kalman filter is also used to estimate the INS error. Two processes are added to the integration scheme: calculation of the a priori position of the GNSS receiver installed on the object based on the current position and the attitude determined by the INS. Further, the SNS makes a correction of the obtained a priori position. In [15], it is proposed to use a multilayer perceptron neural network in combination with the Kalman filter for predicting and estimating the position of GNSS satellites when they are not available. The second technique based on the GPS-aided INS approach is called the tightly coupled integration technique. A tightly coupled integration system also uses a Kalman filter to estimate the error of the INS. However, in contrast to the loosely coupled integration system, the GNSS measurements are used here to estimate the pseudorange, carrier phase or Doppler shift which are then used to construct a complementary measurement vector in the Kalman filter [11; 16; 17; 18]. Accordingly, the complementary measurement vector is the difference between the pseudoranges, carrier phase or Doppler shift measurements determined by the INS and GNSS [7; 8]. This integration scheme is more difficult to numerically implement than a loosely coupled scheme since it is necessary to implement the calculation of ephemeris, the GNSS receiver clock model, etc., to determine the pseudorange, carrier phase or Doppler shift. However, it allows to carry out a partial \"correction\" of the INS error in the presence of limited GNSS measurements since the measurements in the Kalman filter are updated even when measurements from one navigation satellite are available [7; 11]. The third basic technique is called the deeply (or ultra-tight) coupled integration technique that is implemented at the hardware level. A deeply coupled system is an extended version of a tightly connected integration system by supplementing the process of SNS operation with INS functionality [7].