An Interacting Multiple-Model-Based Abrupt Change Detector for Ground-Penetrating Radar
Why this work is in the frame
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Bibliographic record
Abstract
In this letter, we propose an interacting multiple-model (IMM)-based abrupt change detector for ground-penetrating radar (GPR) applications. Ground clutter varies with surface roughness, soil nature, as well as depth of the soil layer, necessitating a multiple-model approach. The IMM is first trained for a chosen number of models and then used to characterize the GPR data. The IMM predictor segments the entire GPR data into regions of identical models and then identifies targets by detecting abrupt changes in model parameters. The number of models is determined using the minimum prediction error criterion. The prediction performance of the IMM predictor is theoretically analyzed, and its detection performance is also evaluated through an receiver operating characteristics analysis to illustrate the improved performance of the proposed detector.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| 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 it