WiProfile: Unlocking Diffraction Effects for Sub-Centimeter Target Profiling Using Commodity WiFi Devices
Bibliographic record
Abstract
Despite intensive research efforts in radio frequency noncontact sensing, capturing fine-grained geometric properties of objects, such as shape and size, remains an open problem using commodity WiFi devices. Prior attempts are incapable of characterizing object shape or size because they predominantly rely on weak signals reflected off objects in a very small number of directions. In this paper, motivated by the observation that the diffracted signals around an object between two WiFi devices carry the contour information of the object, we formulate the problem of reconstructing the 2D target profile and develop WiProfile, the first WiFi-based system that unlocks the diffraction effects for target profiling. We introduce a CSI-Profile model to characterize the relationship between the CSI measured at different target positions and the target profile in the diffraction zone. With suitable approximations, the inverse problem of deriving the target profile from CSI can be solved by the inverse Fresnel transform. To mitigate CSI measurement errors on commodity WiFi devices, we propose a novel antenna placement strategy. Comprehensive experiments demonstrate that WiProfile can accurately reconstruct profiles with median absolute errors of less than 1 cm under various conditions, and effectively estimate the profiles of everyday objects of diverse shapes, sizes, and materials. We believe this work opens up new directions for fine-grained target imaging using commodity WiFi devices.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".