Multi-Target Device-Free Wireless Sensing Based on Multiplexing Mechanisms
Bibliographic record
Abstract
Device-free wireless sensing (DFWS) is a promising technology which could sense target states without requiring them equipped with any device. In recent years, it has drawn considerable attention due to its potential application in the fields of fatigue driving detection, vital sign monitoring, human-computer interaction, etc. State-of-the-art work has achieved excellent sensing performance when there is one target only. However, when there are multiple targets need to be sensed simultaneously, the influenced signals from different targets will be mixed together, and thus traditional DFWS methods will fail. There still lacks an effective system solution to this problem. Inspired by the multiplexing mechanisms utilized in communication systems, in this paper, we explore and exploit the idea of separating and extracting the influenced signal from each target by leveraging three novel multiplexing mechanisms, i.e., angle division multiplexing sensing, range division multiplexing sensing, and source division multiplexing sensing, and thus realize multi-target DFWS accordingly. Meanwhile, we also give theory analysis on the sensing capability of the proposed multiplexing mechanism based multi-target sensing systems. Furthermore, taking multi-target vital sign monitoring as a case study, we develop a 77 GHz FMCW hardware based prototype system, and evaluate the proposed mechanisms extensively. Experimental results reveal the effectiveness of the proposed mechanisms.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".