# A Comprehensive Review of GNSS/INS Integration Techniques for Land and Air Vehicle Applications
## 1 Introduction
In particular, the integration of INS and SNS measurements is performed at the level of combining GNSS RF samples with inertial measurement samples [7; 19]. In general, even in case of using the tightly coupled and deeply coupled integration schemes, it cannot help reducing the accuracy of the integrated system when GNSS signals are unavailable. To solve this problem, many developers offer several approaches. One of the approaches is the use of the ZUPT (Zero Velocity Update) method, based on curve approximation methods for estimating the error in determining the velocity and position of an object using INS [20]. When implementing the proposed approach for moving objects, it may be difficult to approximate curves due to the presence of many rapidly changing parameters, such as time, acceleration and motion trajectory. In most cases, this approach is applicable to objects that make many stops so that the algorithm can work in static mode. For the most part, it is more applicable to the implementation of an integrated INS/GNSS system for pedestrians. Approaches based on the use of additional equipment, such as computer vision [21], odometer [22] or odometer and compass [23], are also much applicable. In particular, in [22], a street return algorithm was proposed that makes it possible to refine the position determined using the INS in the absence of GNSS measurements. In [23], an upgrade and improvement of this algorithm using a compass is proposed. Moreover, methods based on neural networks [24; 25] are widely used. In [24], it is proposed to use a neural network to predict INS errors in determining the velocity and position of an object during restrictions on access to GNSS signals. The authors of [24] propose to use a neural network for simulation of temporarily absent GNSS signals. In connection with the effect of multipath of GNSS signal, as well as with the effects of the ionosphere and troposphere [5; 26], there is a problem associated with the need to reduce the outliers, for example, for pseudorange, leading to a deterioration in the positioning accuracy of an object [27]. To reduce the outliers, the authors of [27] propose a method for estimating the measurement error, which involves the use of a nonlinear filter in real time. In [28], an adaptive robust Kalman filter is proposed that makes it possible to eliminate low-quality measurements and outliers and improve the accuracy of determining the position and velocity of an object. Approaches based on accurate modelling and estimation of INS stochastic errors are also used to improve the accuracy of integrated INS/GNSS systems. Several methods are proposed in open publications, in particular methods based on the use of an autoregressive model, a Gauss-Markov model [29], Allan variance [30] and a combination of the proposed approaches [31]. The paper [32] proposes an INS error model based on two tools: a technique for modeling nonlinear errors called fast orthogonal search (FOS) and the support vector machine (SVM) 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 when GNSS measurements are unavailable. Other tools, such as the robust Kalman filter [33], unscented Kalman filter [34; 35] and complex algorithms based on neural networks [36; 37; 38], are also used for improving the accuracy of the integrated SNS/INS system. The development of integrated INS/GNSS systems in relation to land vehicle is based on the principles listed above. The Kalman filter [6; 39; 40], deeply coupled [40], loosely coupled [41], and tightly coupled [42] integration schemes are used as the main tool for integration. The main features of the application of integrated INS/GNSS systems to vehicles are the limitations caused by the trajectory of motion within the urban space.