Robust Orientation Method Based on Atmospheric Polarization Model for Complex Weather

Abstract

Accurate and reliable navigation data is a key component in Internet of Things (IoT). High-precision and stable autonomous orientation has attracted considerable attention regarding environments in which the global navigation satellite system signal is unreliable. This study proposed a robust orientation method based on the atmospheric polarization mode called weather weighting sparse coding that includes weather classification, sparse coding, and fitting. In comparison with previous studies, the proposed method is effective in restoring the angle-of-polarization image and achieving accurate orientation under complex weather. Specifically, to achieve high-precision orientation in complex weather, a polarization compass was designed, which used the characteristics of the different levels of destruction of atmospheric polarization images under different weather conditions for classification and denoising. The proposed strategy was used to process atmospheric polarization images obtained in complex weather. Experimental results showed that the orientation accuracy was better than 0.31° (root-mean-square error) under conditions of overcast, sandstorms, clear with overexposed areas, and smog with tree-obscured and overexposed areas.