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Inspired by the use of polarized information for navigation behaviors in nature, many researchers have carried out a lot of researches on bionic polarized light navigation. Due to the complexity of the atmospheric radiation transmission process, there is a lot of uncertainty for the bionic polarized light navigation method that accurately calculates the polarization information of the polarized skylight pattern pixel by pixel. This paper studies the distribution of the polarized skylight pattern under different weather conditions through a large number of observational experiments. It is found that the distribution of the angle of polarization shows a stable &#x221E; characteristic, which can reflect the macro distribution characteristics and changing laws of the polarized skylight pattern. This paper extracts the &#x221E; characteristic from the polarization angle image, then we establish a model of &#x221E; characteristic to solve the solar position by calculating the feature similarity indexes between the measured &#x221E; characteristic images and Rayleigh &#x221E; characteristic images. And we propose an improved harmony search algorithm to solve the optimal solution of the model. By searching in the Rayleigh &#x221E; characteristic image database, the solution vector corresponding to the Rayleigh &#x221E; characteristic image with the highest feature similarity index is the solar position. In this paper, experiments are performed on measured polarized images under sunny and cloudy weather, and the experimental results verify the effectiveness of the proposed algorithm.

Solar Position Acquisition Method for Polarized Light Navigation Based on ∞ Characteristic Model of Polarized Skylight Pattern