Bio-Inspired 3-D Attitude Measurements Algorithm Based on the Polarized Image Sensor

Polarized compass technology is a navigation method based on polarized image processing, characterized by electromagnetic interference resistance and absence of cumulative errors. However, current 3D orientation determination techniques for polarized compasses demonstrate limited anti-occlusion capability against sky polarization patterns under cloudy conditions, where cloud obstruction disrupts atmospheric polarization distribution, resulting in complete failure of 3D orientation solutions. This significantly restricts the environmental adaptability of polarized compass systems. To address this challenge, this paper first theoretically analyzes the atmospheric polarization distribution patterns under cloudy environments, establishing mathematical models of the polarization meridian, polarization neutral points, and solar position. Building upon this foundation, a novel 3D compass technique incorporating simulated annealing algorithm-based neutral point extraction is proposed. Experimental results demonstrate that when 55% of the polarization image is occluded by clouds, conventional 3D polarized orientation determination algorithms fail completely, while the proposed method successfully achieves device orientation with roll, pitch, and yaw angle errors below 1°. This advancement significantly expands the application scenarios for polarized navigation technology.