Design of Adaptive Thresholds For One-Bit Radar Imaging Based on Adversarial Samples
Bibliographic record
Abstract
In this paper, we present a new scheme of adaptive quantization thresholds for one-bit radar imaging based on adversarial samples. Radar imaging with one-bit compressive sensing (CS) is attractive due to the small storage burden and low requirements to the analog-to-digital converter. However, conventional one-bit quantization scheme with fixed thresholds does not use the magnitude information, possibly leading to difficulty in energy estimation and higher amplitude recovery error. Recently, adaptive thresholds methods have been developed to deal with the limitation of fixed thresholds scheme. Based on the adversarial training theory, the proposed new method embeds adversarial samples into the binary iterative hard thresholding (BIHT) algorithm and exploits an adaptive thresholds scheme based on the adversarial samples to improve the model robustness and imaging quality with one-bit coded data. Simulation results demonstrate that the proposed method outperforms the BIHT with fixed thresholds in one-bit radar imaging.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".