One-Bit Radar Imaging Via Adaptive Binary Iterative Hard Thresholding
Bibliographic record
Abstract
One-bit radar imaging has received much attention due to the low cost of the analog-to-digital converter (ADC) and the low storage and transmission burden. The one-bit radar imaging results using conventional one-bit compressive sensing (CS) algorithms, such as the binary iterative hard thresholding (BIHT) algorithm, are always contaminated by artifacts, especially under noisy conditions. In this paper, we present an adaptive-BIHT (A-BIHT) algorithm to mitigate artifacts and improve the one-bit radar imaging performance. In the proposed A-BIHT algorithm, we devise a quantization level parameter, and update the quantization level parameter and the imaging result in an iterative fashion by employing a relaxed quantization consistency condition. The relaxed quantization consistency condition is designed to allow some noisy one-bit measurements to be inconsistent. In this way, the proposed algorithm mitigates the effect of noise on consistent reconstruction, and thus, alleviates artifacts and improves the imaging quality. Simulations and experimental results demonstrate that the proposed A-BIHT method can provide superior imaging performance with suppressed artifacts compared with the conventional BIHT method.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 | 0.000 |
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".