Bioinspired polarized light compass in moonlit sky for heading determination based on probability density estimation |
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Authors: | Yueting YANG Yan WANG Lei GUO Bo TIAN Jian YANG Wenshuo LI Taihang CHEN |
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Institution: | 1. School of Automation Science and Electrical Engineering, Beihang University, Beijing 100083, China;2. Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine and Engineering, Beihang University, Beijing 100083, China;3. Key Laboratory of Big Data-Based Precision Medicine, Ministry of Industry and Information Technology, Beihang University, Beijing 100083, China;4. Hangzhou Innovation Institute, Beihang University, Beijing 100083, China;5. School of Cyber Science and Technology, Beihang University, Beijing 100083, China |
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Abstract: | Bioinspired polarized skylight navigation, which can be used in unfamiliar territories, is an important alternative autonomous navigation technique in the absence of Global Navigation Satellite System (GNSS). However, the polarization pattern in night environment with noise effects and model uncertainties is a less explored area. Although several decades have passed since the first publication about the polarization of the moonlit night sky, the usefulness of nocturnal polarization navigation is only sporadic in previous researches. This study demonstrates that the nocturnal polarized light is capable of providing accurate and stable navigation information in dim light outdoor environment. Based on the statistical characteristics of Angle of Polarization (AoP) error, a probability density estimation method is proposed for heading determination. To illustrate the application potentials, the simulation and outdoor experiments are performed. Resultingly, the proposed method robustly models the distribution of AoP error and gives accurate heading estimation evaluated by Standard Deviation (STD) which is 0.32° in a clear night sky and 0.47° in a cloudy night sky. |
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Keywords: | Nocturnal polarization Moonlit sky Angle of Polarization (AoP) Probability density estimation Navigation |
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