Bio-Inspired Polarization Compass for Solar Azimuth Prediction Under Clear and Cloudy Sky Conditions
Bibliographic record
Abstract
Sunlight becomes partially polarized due to Rayleigh scattering while passing through the atmosphere. Many insects such as ants and beetles utilize the polarization information of skylight for navigation. Unlike magnetic compass and GPS, this scheme is free from interference and unlike inertial schemes, its error does not accumulate with time. In recent years this navigation scheme has received a lot of attention for navigation of aerial and terrestrial vehicles. Development of a polarization compass that can provide accurate heading information in different weather conditions will benefit a wide range of applications. Here we report the application of linear regression for implementation of a polarization compass. The model is trained with real sky images and then used to predict the solar azimuth in both clear and cloudy sky conditions. The root mean square errors for both conditions were less than 1°. We also compared the performance of the proposed scheme with that of Hough transform and support vector machine, which have been successfully utilized for the same application in the past. Linear regression outperformed Hough transform for all sky conditions considered, and its performance was comparable with support vector machine. However, unlike the other two methods considered, the accuracy of linear regression will increase significantly when trained with a large set of sky images. Therefore, with sufficient training, linear regression can be a promising option for implementation of polarization compass.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".