A Fundamental-and-Harmonic Dual-Frequency Doppler Radar System for Vital Signs Detection Enabling Radar Movement Self-Cancellation
Bibliographic record
Abstract
This paper proposes and presents a cost-effective fundamental-and-harmonic dual-frequency (FHDF) Doppler radar system for vital signs detection from a mobile radar platform. The proposed FHDF radar architecture concurrently transmits the fundamental signal component (FSC) and its inherent second harmonic signal component (SHSC) of the voltage-controlled oscillator toward opposite directions. The FSC is transmitted toward a target in motion, while the SHSC is transmitted toward a stationary low-cost reflector. For the receiver, a coherent dual-band dual-low-IF architecture is derived and proposed for the first time. This architecture can concurrently receive the reflected FSC that is phase modulated by both motions of the target and the radar platform, and the reflected SHSC that is phase modulated only by the motion of the radar platform, without aliasing, thus enabling the reduction of the overall radar size, power consumption, and system cost. Through the subtraction of a pertinent information contained in the received SHSC from the information embedded in the received FSC using an adaptive noise cancellation technique, the resulting signal contains the desired information that is free of motion artifacts from a moving radar platform. Experimental results show that the proposed radar system is capable of extracting human vital signs, including respiration and heartbeat, even in the presence of a large radar platform movement. By using the proposed FHDF radar system, a see-through-wall vital signs detection from a mobile radar platform can also be successfully conducted without using any additional sensors.
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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 | 0.000 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".