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    This study introduces an orthogonal vector algorithm for deriving the solar vector from sky polarization patterns, crucial for animal navigation. The new method performs comparably to or better than existing algorithms, with minimal solar-vector angle deviations observed.

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    Area of Science:

    • Animal navigation
    • Bio-inspired sensing
    • Optics

    Background:

    • Sky polarization patterns serve as vital long-distance navigation cues for animals like insects and birds.
    • The solar vector, essential for navigation, can be derived using the single-scattering Rayleigh model.

    Purpose of the Study:

    • To propose and evaluate a novel orthogonal vector algorithm for solar vector determination from sky polarization.
    • To compare the performance of the proposed algorithm against existing least-squares methods.

    Main Methods:

    • Developed an orthogonal vector algorithm utilizing the redundancy of the single-scattering Rayleigh model.
    • Simplified the 3D problem by assuming coplanarity of polarization vectors.
    • Constructed a device with polarized-light sensors and a 3D attitude sensor for experimental validation.

    Main Results:

    • The orthogonal vector algorithm demonstrated performance equal to or better than two other least-squares algorithms.
    • Experimental data showed small solar-vector angle deviations (mean of 0.242°) when polarization vector intersection angles approached 90°.
    • The assumption of coplanarity was supported by experimental data.

    Conclusions:

    • The proposed orthogonal vector algorithm is effective for determining the solar vector from sky polarization.
    • The algorithm's performance is robust, even with the inherent ambiguity in polarization vector directionality.
    • The findings support the use of sky polarization for navigation and provide a reliable method for solar vector estimation.