Clutter Elimination and Harmonic Suppression of Non-Stationary Life Signs for Long-Range and Through-Wall Human Subject Detection Using Spectral Kurtosis Analysis (SKA)-Based Windowed Fourier Transform (WFT) Method
Bibliographic record
Abstract
Life sign detection is important in many applications, such as locating disaster victims. This can be difficult in low signal to noise ratio (SNR) and through-wall conditions. This paper considers life sign detection using an impulse ultra-wideband (UWB) bio-radar with an improved sensing algorithm for clutter elimination, harmonic suppression and random-noise de-noising. To improve detection performance, two filters are used to improve SNR of these life signs. The automatic gain method is performed in fast time to improve the respiration signals. The spectral kurtosis analysis (SKA)-based windowed Fourier transform (WFT) method and an accumulator in the frequency domain are used to provide two distance estimates between the radar and human subject. Further, the accumulator can also provide the frequency estimate of the respiration signals. These estimates are used to determine if a human is present in the detection environment. Results are presented which show that the range and respiration frequency can be estimated accurately in low signal to noise and clutter ratio (SNCR) environments. In addition, the performance is better than with other techniques given in the literature.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".