Detection of human targets behind the wall based on singular value decomposition and skewness variations
Bibliographic record
Abstract
A novel method for stand-off detection and localization of human targets behind the wall using a monostatic ultra wideband (UWB) radar is proposed. In this method, Singular Value Decomposition (SVD) and skewness are employed to achieve detection and localization. Identification of possible bins that may contain the target is done using the SVD while decision about the presence of the target in the identified bin is made using skewness. After preprocessing of the signal, skewness of the radar returns over the scans at every bin is calculated before applying this methodology. In this method, the contributions of the clutter is removed to enhance the returns from the target, by removing the dominant singular values iteratively and the range profiles over scans are reconstructed at each iteration. After each iteration, the energy in the bin over the scans is compared and the bin with maximum energy is identified as a potential target location and the previously determined skewness at this bin is compared against a precomputed data-dependent threshold. A target is declared detected if the skewness at the selected bin is lower than the threshold. The proposed method is applied on 46 measurements with a single target behind 20 cm thick gypsum wall. This method produced a 0% probability of error type I (False detection) and 4.34% error type II (missed detection) while detecting single targets. Using the same approach, it was also possible to discriminate between two targets standing 0.3 m away from each other and 3.5 m behind a 20 cm thick gypsum wall.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".