Adaptive method for estimating information from a polarized skylight

Solar position detection method by bionic polarized light compass

To address the needs of polarized light navigation for accurate position information of feature points in the sky, an accurate solar position detection method based on an all-sky polarization pattern imaging system is proposed. Unlike the traditional spot-based solar position detection method, this method uses the polarization information inherent in the atmosphere to accurately measure solar position. This approach is characterized by simple detection, high accuracy, and wide application range. The optical acquisition system is composed of three miniature large-field camera modules and polarizers, which enables a more compact structure, smaller size, and lesser height. Based on this principle, the solar position solution algorithm was simulated and then verified in various weather environments using the optical acquisition system built as part of this study. Solar position was detected at different moments on the same day in clear weather, and the accuracy of the measured solar altitude and azimuth angles was 0.024° and 0.03°, respectively. The accuracy of the measured solar altitude and azimuth angles was 0.08° and 0.05°, respectively, when the sun was shielded by high-rise buildings and 0.3° and 0.1° when the sun was shielded by branches and tree leaves. Aerosol concentrations exceeding a certain amount destroyed the Rayleigh distribution pattern of polarized light, thus affecting solar position detection accuracy. It is concluded that this novel detection method can not only meet the needs of polarized light navigation for solar position, but also provide a new exploration idea for enthusiasts who are eager to explore the mysteries of the universe.

Modeling the celestial distribution of skylight polarization patterns by incorporating the influence of both the sun and the moon through an analytical model

The orientation of many polarization-sensitive animals and the hypothetical sky-polarimetric Viking navigation both rely on the polarization pattern of skylight. For 40 years, scientists have attempted to construct various models to simulate this pattern. However, existing theoretical models have only analyzed the polarization pattern of skylight that is influenced separately by the sun or the moon and have built their modeling frameworks based on the position of one light source. This approach fails to account for the combined influence of the sun and the moon on the distribution of skylight polarization patterns at certain times. In fact, ignoring the influence of the moon during the dawn and dusk periods in clear weather conditions may lead to significant errors in the simulation results compared to the measured data. In this paper, we present an analytical model that considers various factors, including skylight intensity, horizon correction factor, atmospheric turbidity condition, and combined influence of both the sun and moon on the distribution of polarized skylight. We believe our model demonstrates enhanced agreement with measured data and will further our understanding of how animals use the celestial polarization pattern for navigation, particularly when both the sun and the moon appear in the sky. Moreover, the findings of this study may facilitate the advancement of bio-inspired navigation systems.

High-similarity analytical model of skylight polarization pattern based on position variations of neutral points

The skylight polarization pattern contains rich information for navigation, meteorological monitoring, and remote sensing. In this paper, we propose a high-similarity analytical model by considering the influence of the solar altitude angle on the neutral point position variations for the distribution pattern of the polarized skylight. A novel function is built to determine the relationship between the neutral point position and solar elevation angle based on a large number of measured data. The experimental results show that the proposed analytical model achieves a higher similarity to measured data compared with existing models. Furthermore, data from several consecutive months verifies the universality, effectiveness, and accuracy of this model.