# GNSS/INS Integration Based on Machine Learning LightGBM Model for Vehicle Navigation
## 1 Introduction  
Navigation and positioning are among the most widely used technologies in intelligent transportation systems, advanced vehicle control, and vehicle safety systems [1]. The main idea behind their algorithms is to use satellite data and vehicle dynamics data to calculate the current specific position of the vehicle. An algorithm's robustness determines the accuracy of the information output from the vehicle navigation system and its ability to adapt to the environment. GNSS/INS integrated navigation is a low-cost, highly accurate, and versatile integrated navigation solution [2]. Global navigation satellite systems (GNSS) provide real-time information on the position of a vehicle, primarily via satellites. The inertial navigation system first gathers information about angular and linear motion relative to inertial space. It then uses inertial navigation differential equations to calculate vehicle speed and position changes. The GNSS and INS are complementary in many ways [3]: (1) The raw vehicle dynamics data measured by the INS are often noisy. The vehicle position information obtained through independent INS solving can drift over time, requiringexternal assistance to calibrate the INS data. GNSS systems, on the other hand, can provide long-term stable data when the signal is available and can be used to limit INS errors. (2) There could be GNSS outages in practical operation due to signal blockage or interference, such as the presence of buildings, tunnels, forest cover, etc. In this case, the vehicle cannot obtain the data provided by the GNSS. Hence, continuity data provided by the INS can compensate for the lack of position data in the event of a GNSS outage. (3) The data provided by the GNSS for the vehicle are updated less frequently, while the INS provides accurate short-term data at a very high rate, which can be used to interpolate GNSS trajectories. (4) The INS provides the complete vehicle state (e.g., position, speed, attitude, etc. ), whereas a single GNSS receiver cannot provide angular information about the vehicle. These are also the reasons for creating vehicle integrated navigation algorithms that integrate GNSS and INS. However, the accuracy of the integrated navigation algorithm could be inevitably reduced by a long-time outage of GNSS. As a result, the solution to this problem has emerged as a hot topic in integrated navigation algorithm research. The approaches to vehicle integrated navigation algorithms can be classified into two categories. One category is the vehicle integrated navigation algorithms based on Bayesian filtering techniques. Kalman filtering (KF) is one of the most common implementations. [PERSON] et al. applied a dual-filter smoother to an extended Kalman filter (EKF)-based INS/GPS integrated navigation algorithm for vehicles. The authors found that the algorithm provided accurate navigation parameters in the presence of GPS signal blockage, thereby improving the overall data fusion [4]. [PERSON] et al. proposed a MEMS-INS/GNSS integrated navigation algorithm for vehicles combined with Allan variance analysis under non-complete constraints. The resulting algorithm is applicable to the loose/tight integrated navigation method for vehicles with specific fault detection and troubleshooting capability [5]. [PERSON] et al. proposed the attitude and heading reference system (AHRS) algorithm for an INS/GPS loose integrated navigation system, which was found to suppress the divergence of the INS solution during a GPS outage [6]. [PERSON] et al. proposed an SINS/OD integrated navigation algorithm for vehicles based on the state transformation extended Kalman filter (ST-EKF). The authors discovered that it achieved higher positioning and heading angle accuracy than the conventional EKF algorithm [7]. [PERSON] et al. used an improved cubature Kalman filter (CKF) algorithm based on the sigma point update framework for GNSS/INS integrated navigation and found that it improved the reliability of the navigation system in a GNSS-challenged environment [8].