Enhanced Super-Resolution DOA Estimation via Aperture Extrapolation in RIS Technology for ITS Applications
Bibliographic record
Abstract
The reconfigurable intelligent surface (RIS) technology offers transformative potential for wireless communications, sensing, and vehicle localization within intelligent transportation systems (ITSs). Accurate direction-of-arrival (DOA) estimation, especially for resolving closely spaced vehicles, is critical for safe autonomous driving. This article introduces aperture extrapolation for RIS-based DOA estimation (APEX-RIS), a high-resolution DOA estimation technique that integrates 2-D linear prediction with 2-D unitary Estimation of signal parameters via rotational invariance technique (ESPRIT) to virtually extend uniform rectangular arrays (URAs). This virtual aperture extrapolation (APEX) significantly enhances the spatial resolution beyond conventional methods without requiring physical array expansion. APEX-RIS performance is validated through extensive simulations across varying signal-to-noise ratios (SNRs), snapshot counts, and angle separations. Results demonstrate substantial improvements in probability of resolution (PR) and root-mean-square error (RMSE), particularly in low-SNR and global positioning system (GPS)-denied environments. The method exhibits superior accuracy in resolving closely spaced sources while maintaining the computational efficiency for real-time ITS applications. While increasing extrapolation gain boosts performance, it also raises computational complexity. APEX-RIS represents a significant advancement in RIS-assisted DOA estimation, supporting safer, more efficient autonomous vehicle (AV) deployment and robust ITS infrastructure.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".