A scalable sphere-constrained magnitude-sparse SAR imaging
Bibliographic record
Abstract
The classical synthetic aperture radar (SAR) imaging techniques based on matched filters are limited by data bandwidth, resulting in limited imaging performance with side lobes and speckles present.To address the high-resolution SAR imaging problem, sparse reconstruction has been extensively investigated.However, the state-of-the-art sparse recovery methods seldom consider the complex-valued reflectivity of the scene and only recover an approximated real-valued scene instead.Furthermore, iterative schemes associated with the sparse recovery methods demand a high computational cost, which limits the practical applications of these methods.In this paper, we establish a sphere-constrained magnitude-sparsity SAR imaging model, aiming at enhancing the SAR imaging quality with high efficiency.We propose a non-convex non-smooth optimization method, which can be accelerated by stochastic average gradient acceleration to be scalable with large-scale problems.Numerical experiments are conducted with point-target and extended-target simulations.On the one hand, the point-target simulation showcases the superiority of our proposed method over the classical methods in terms of resolution.On the other hand, the extended-target simulation with random phases is considered to be in line with the practical scenario, and the results demonstrate that our method outperforms the classical SAR imaging methods and sparse recovery without phase prior in terms of PSNR.Meanwhile, owing to the stochastic acceleration, our method is faster than the existing sparse recovery methods by orders of magnitude.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.002 | 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".