A dynamic STC attenuation strategy based on environmental perception
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract The fixed static STC suppression strategy is often applied in project practice. There is frequently an issue where clutter procession algorithms fail to swiftly adapt to environmental changes, leading to an increased false alarm rate in radar systems, which significantly impairs the normal detection of targets and, consequently, directly undermines the tactical effectiveness of air defense operational command systems. The paper discusses that signal processing is used to process the data before and after the attenuation of STC in the full range section respectively, and the attenuation value in the full range section is quantified according to the minimum scale. After multiple iterations, an attenuation benchmark is set for different range segments to enable dynamic selection of STC (Sensitivity Time Control) attenuation strategies. This enhances the adaptive and rapid matching capabilities of clutter suppression strategies to changing environments, achieving a dynamic balance between target detection probability and false alarm rate. Ultimately, it realizes the radar’s clutter environment perception and adaptive clutter processing capabilities, enabling the radar to maintain superior target detection performance in cluttered or interfered environments.
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.
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