Ultraviolet-visible light compass method based on local atmospheric polarization characteristics in adverse weather conditions

Abstract

Bionic polarization navigation has attracted extensive attention because of its strong anti-interference performance and no accumulation of errors over time. However, very few studies have fully considered the influence of adverse weather conditions such as cloudy and overcast weather, which play a key role in navigation accuracy. Therefore, we propose an adaptive ultraviolet-visible light compass method based on local atmospheric polarization characteristics applicable to various weather conditions. The proposed method transforms the heading determination problem into a multiclassification problem by using a weather recognition technique. Ultraviolet detection is used to weaken the depolarization effect of cloud particles and to obtain more accurate skylight polarization patterns. Then, on the basis of screening effective data, the sun direction vector is calculated by using the electric vector direction and is finally combined with the astronomical calendar to achieve navigation. The experimental results confirm that, compared to the other methods, the designed algorithm can suppress the interference of clouds better and adapt to complex weather conditions. Under cloudy and overcast conditions, the heading angle error is reduced to less than 2°.