The Integration of Photodiode and Camera for Visible Light Positioning by Using Fixed-Lag Ensemble Kalman Smoother
Bibliographic record
Abstract
Visible Light Positioning (VLP) has become one of the most popular positioning and navigation systems in this decade. Filter-based VLP systems can provide real-time solutions but have limited accuracy. On the contrary, fixed-interval smoothers can help VLP achieve higher accuracy but require post-processing. In this article, a trade-off solution, Fixed-Lag Ensemble Kalman Smoother (FLEnKS), is proposed for VLP to achieve a semi-real-time and accurate positioning solution. The forward part of the FLEnKS is based on the Ensemble Kalman Filter (EnKF), which uses stochastic sampling with ensemble members and enables a better reflection of the features of nonlinear systems. The backward filter in the FLEnKS compensates for the estimation error from the forward filter using the linearization based on error states and further reduces the estimation error. Furthermore, multiple data from both photodiode (PD) and camera are fused in the proposed FLEnKS for VLP, which further improves the accuracy of conventional VLP with a single data source. Preliminary field test results show that the proposed FLEnKS provides a semi-real-time positioning solution with the average 3D positioning accuracy of 15.63 cm in dynamic tests.
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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 | 0.001 |
| 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 | 0.001 | 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".