Optimal Optical Omnidirectional Angle-of-Arrival Estimator With Complementary Photodiodes
Bibliographic record
Abstract
Angle-of-arrival (AOA) estimator is the core device in visible light positioning (VLP) systems with AOA algorithms. However, existing AOA estimators suffer from high computational complexity, narrow field-of-view (FOV), low accuracy, or high power consumption. In this work, we propose a novel AOA estimator based on an array of tilted complementary photodiodes (CPDs), where the estimator's FOV can be $2\pi$ rad, and the AOA estimation only requires the solution of a linear equation set. The orientations of the CPDs in the AOA estimator are optimized with respect to the average error power, resulting in closed-form optimal orientation expressions for an arbitrary number of CPDs. We also derive closed-form expressions for the probability density function and the cumulative distribution function (CDF) of the AOA estimation error. On the basis of CDF expression, we derive closed-form asymptotic bounds for the positioning outage probability of a fundamental VLP system. Analytical, simulation, and experimental results verify that the optimal AOA estimator can minimize the estimation error, and it can be employed in AOA positioning systems perusing high accuracy, low complexity, large FOV, low cost, low power consumption, and high response speed.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".