# Research on the SSUKF Integrated Navigation Algorithm Based on Adaptive Factors
## 1 Introduction  
The Global Navigation Satellite System (GNSS), as a satellite-based positioning system, is widely applied in fields such as transportation, communications, and scientific research. The GNSS determines an object's position, velocity, and time by receiving satellite signals, offering precise global positioning services without geographic constraints. It provides stable satellite signals in diverse environments, such as cities, oceans, and remote areas, enabling high-precision positioning [1]. However, despite its capability to provide decimeter-level or higher accuracy in open environments, the GNSS still faces some notable shortcomings and challenges in practical applications. Firstly, GNSS signals are easily affected by obstacles, interference, and multipath effects [2]. Particularly in urban canyons, tunnels, and densely wooded areas, signal transmission becomes unstable, significantly reducing positioning accuracy. In these environments, satellite signal transmission is severely restricted, preventing the system from delivering accurate locationinformation. Furthermore, GNSS positioning accuracy can also degrade under extreme weather conditions. To address these issues, the inertial navigation system (INS) has emerged as an important complement to the GNSS, serving as an autonomous navigation solution. The INS does not rely on external signals and uses built-in inertial sensors, such as accelerometers and gyroscopes, to measure an object's acceleration and angular velocity, thereby calculating its position, velocity, and orientation [3]. Since INS operates independently of external signals, it can still provide reliable navigation information in GNSS-denied environments (such as underground, in tunnels, or within buildings). The INS is characterized by its high-frequency response, making it suitable for highly dynamic environments, and it can provide relatively high positioning accuracy over short periods. However, the main drawback of the INS is the accumulation of errors [4]. Over time, drift and noise from the inertial sensors lead to the gradual accumulation of system errors, which affects navigation accuracy. Therefore, relying solely on the INS for positioning results in a decline in accuracy over time, making it difficult to maintain high-precision navigation over extended periods. To overcome the limitations of both the GNSS and INS, GNSS/INS integrated navigation technology has become a research focus in recent years [5]. By combining these two systems, their respective strengths can be fully utilized, compensating for the shortcomings of each when used independently. Specifically, the GNSS provides high-precision positioning information on a global scale but is limited in certain environmental conditions; the INS, on the other hand, can provide continuous navigation information when the GNSS is unavailable. By integrating the advantages of both, more accurate and stable navigation results can be achieved in complex environments. In integrated navigation systems, data fusion algorithms play a crucial role. Their primary task is to effectively integrate data from different sensors (such as the GNSS, the INS, and visual sensors) to provide more accurate and reliable navigation information. Common data fusion algorithms include Kalman Filtering [6] (KF), Extended Kalman Filtering [7] (EKF), and Particle Filtering [8] (PF), which optimize the weighted fusion of sensor data to reduce noise effects and allow real-time updates in dynamic environments. However, Kalman Filtering requires precise mathematical models and accurate prior noise statistics, and when noise characteristics deviate from those expected, filtering accuracy may deteriorate or even diverge [9]. To address this issue, researchers have made numerous improvements to the Kalman Filtering algorithm [10].