# Research on GNSS/IMU/Visual Fusion Positioning Based on Adaptive Filtering
## 1 Introduction  
In modern navigation and positioning, precise and reliable positioning capabilities are a critical requirement for numerous applications, such as autonomous driving, unmanned aerial vehicle (UAV) flight, and mobile robot navigation. Accurate navigation primarily relies on the Global Navigation Satellite System (GNSS) [1; 2; 3], with well-known satellite systems, such as GPS and BeiDou, primarily utilizing medium Earth orbit (MEO) satellites to transmit signals and provide positioning services to ground users. However, in urban environments, signal blockage by tall buildings and multipath effects [4] make it challenging to achieve precise positioning using MEO satellite signals in certain situations. In contrast, low Earth orbit (LEO) satellites have shorter signal propagation paths, and their signal coverage characteristics can to some extent compensate for the deficiencies of MEO GNSS in these regions. [PERSON] et al. [5] analyzed the Doppler frequency shift measurement accuracy of three LEO satellite constellations (Iridium-Next, Orbcomm, and NOAA) based on actual signal data, and evaluated their potential for combined positioning. The experimental results showed that using all three satellite constellations provided the best positioning performance. Additionally, there is a LEO coarse positioning method based on opportunistic signal Angle of Arrival (AOA) estimation. This approach utilizes the receivable opportunistic signals emitted by LEO satellites, where opportunistic signals refer to those that were not originally intended for positioning purposes, but can be \"opportunistically\" leveraged to obtain relevant information, such as LEO satellite communication signals or broadcast signals. By estimating the AOA of these signals at the receiver, a coarse positioning of the target can be achieved. [PERSON] et al. [6] introduced a novel coarse positioning system based on the estimation of the Angles of Arrival (AoAs) ofopportunistic signals from near-Earth orbit spacecraft. The system employs a dedicated hardware architecture to perform phase interferometric measurements and execute real-time AoA estimation. The study evaluates the accuracy of the AoA estimation and the resulting positioning error, demonstrating the effectiveness of this system for real-time coarse positioning. [PERSON] et al. [7] utilized pseudo-range and Doppler measurements from LEO satellites, combined with a tactical-grade inertial measurement unit (IMU) on a ground vehicle, for navigation when GNSS signals were unavailable. The results showed that the vehicle's position error could reach hundreds of meters in the absence of GNSS signals, but such large positioning errors are unacceptable in daily life, necessitating the integration of other high-altitude satellites and sensors to achieve higher positioning accuracy. As technology advances, modern smartphones are equipped with various sensor chips, such as satellite signal receivers and MEMS IMU, which can aid positioning research. [PERSON] et al. [8] used two different smartphone models to measure GNSS signals and perform real-time positioning, achieving decimeter-level accuracy when a Continuously Operating Reference Station (CORS) network was available. However, in urban canyon environments, where satellite signals are obstructed, using only the smartphone's GNSS receiver is insufficient to achieve decimeter-level accuracy, and sensor fusion is required to improve the positioning precision. [PERSON] et al. [9] leveraged the complementary characteristics of GNSS and Inertial Navigation System (INS), employing a tightly coupled Kalman filter [10] to fuse GPS and INS, simplifying the system complexity and overcoming the problem of INS navigation accuracy degradation over time. In urban canyon environments, if satellite signals are lost for an extended period, GNSS will not be able to effectively correct the consumer-grade MEMS IMU in the smartphone, and the GNSS/IMU integrated positioning results will diverge.