Frequency Domain Image Reconstruction for Imaging With Multistatic Dynamic Metasurface Antennas
Bibliographic record
Abstract
Dynamic metasurface antennas (DMAs) have recently been introduced as a computational imaging (CI) platform that offers significant advantages over conventional imaging systems. Such antennas are able to produce tailored radiation patterns that are later used to encode the scene’s information into a few measurements. However, since the signal is compressed, image reconstruction in the frequency domain using the conventional range migration algorithm (RMA) cannot be directly applied to CI-based apertures synthesized with DMAs. More sophisticated algorithms need to be developed to overcome this limitation, which transfers the complexity of such systems to the software layer. This paper proposes a new multistatic DMA RMA (MD-RMA) technique that achieves decompression of the compressed signals collected from multistatic DMA apertures and processes the decompressed data in the Fourier domain followed by a multistatic-to-monostatic conversion. Simulation results verify that the proposed approach can produce high quality 3D radar images, which alleviates the need to mechanically move the aperture. Moreover, since the image reconstruction is fully conducted in the frequency domain, leveraging the fast Fourier transform (FFT) algorithm, the complexity of the algorithm, and thus the execution time, is significantly reduced in contrast with conventional spatial domain 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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".