Accurate Indoor Localization Assisted With Optimizing Array Orientations and Receiver Positions
Bibliographic record
Abstract
Owing to the multiple antennas equipped at modern Wi-Fi infrastructures, the angle-of-arrival (AoA) based indoor localization systems have successfully achieved the accuracy of tens of centimeters. However, the high accuracy is acquired at the cost of employing the additional resources in the domains of frequency, space or time, which requires complex processing and hinders the practical application. In this paper, we present the design and implementation of RcLoc, which takes full advantages of the flexible array orientations and receiver positions, based on limited resources. Particularly, RcLoc devises a receiver configuration scheme for guiding the system deployment. Optimized array orientation could effectively improve the AoA estimation accuracy and well-designed receiver positions contribute to the Cramer-Rao lower bound of localization error. In the stage of system realization, we further devise an array calibration method to calibrate the actual array and develop an improved AoA estimation algorithm, which make RcLoc robust to the array arrangement. We prototype RcLoc on commodity Wi-Fi devices without manual intervention. Comprehensive experiments in a multipath-rich indoor environment show that RcLoc achieves a median localization accuracy of 0.4 m, which provides useful insights for receiver deployment.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".