LRSMTD: Low-Rank Plus Sparse Multiple-Term Decomposition of Defocusing Target Detection for Single-Channel Single-Band Single-Pass VideoSAR
Bibliographic record
Abstract
Machine learning-based automatic target detection in video synthetic aperture radar (VideoSAR) has great potential for raising the reconnaissance capability in dynamic region of interest (DROI). In this article, a novel systematic perspective, called low-rank plus sparse multiple-term decomposition (LRSMTD) for simultaneously single-channel, single-band, and single-pass (SCSBSP) VideoSAR configuration is proposed to track the ground defocusing targets. To address the target features of circular VideoSAR imaging, we extend the polar format algorithm (PFA) via exploiting a priori knowledge. In accordance with both the revealed imaging and imagery characteristics, we solve the systematic LRSMTD with proximal exchange-based alternating directions method of multipliers (PEADMM), which makes the process interpretable for defocusing target detection. Comprehensive circular SCSBSP airborne VideoSAR experiments reveal the superior detection performance of the systematic LRSMTD and its several subalgorithms with PEADMM, outperforming 11 state-of-the-art algorithms.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".