Iterative Joint Global Polarization Position Strategy Based on Image Feature and Polarization Pattern

In instances where satellite signals are obstructed or unavailable, long-endurance unmanned aerial vehicles (UAVs) face substantial navigational challenges. In an attempt to address this challenge, a biologically inspired global polarization positioning technology has been reported. While this technology offers a possible solution for autonomous global positioning, its practical application faces challenges due to the current limitations in positioning accuracy. Therefore, this article introduces an iterative joint global polarization position strategy based on image features and polarization patterns, aiming to achieve enhanced precision in global positioning. First, a comprehensive polarization positioning model is constructed and the technical challenges are revealed. Subsequently, a solar image position solution method based on joint polarization pattern and image gray value is proposed, which can accurately calculate the solar image position in a single frame. Finally, a solar position iterative solution method is proposed and verified through outdoor experiments. Experimental results of more than 9 h in six cities show that the proposed method improves the current polarization positioning accuracy, and the root mean square error of global positioning is 25.43 km. Overall, this model and algorithm have demonstrated the ability to accurately calculate the latitude and longitude of the carrier in real-time, while exhibiting strong adaptability to diverse environmental conditions.