I/Q Imbalance Compensation and Block-Sparsity-Based Harmonic Analysis with Applications to Vital Signs Estimation using CW Radar Systems
Bibliographic record
Abstract
Recently, breathing and heart rate estimation based on radar technologies have become increasingly popular. Millimeter wave radars are compact, low-cost and can perform vital signs estimation without the necessity of having physical contact with the subject under test. However, the strong harmonics of the breathing signal can contaminate the weak heart signal and make the estimation of the heart rate challenging. In this paper, we present the complete theory for vital signs estimation and address two challenging problems in this field, namely In-phase and Quadrature (I/Q) imbalance compensation as well as heart rate estimation in the presence of strong harmonics of the breathing signal. We address the heart rate estimation based on block sparsity model and cast the problem as a convex mixed-integer programming which can be solved efficiently. Finally, we p resent the experimental results gathered from a Continuous Wave (CW) radar operating at 24 GHz. The ground-truth signal to validate the overall accuracy and effectiveness of the algorithms is provided by the Electrocardiogram (ECG) device. Index Terms-I/Q imbalance compensation, block sparsity, breathing and heart rate estimation, convex mixed-integer programming, CW radars.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".