RCI-DUNet: Efficient Complex-Valued Deep Unrolling Network for Multiview MIMO Radar Coincidence Imaging
Bibliographic record
Abstract
In the application of radar coincidence imaging (RCI) sparse reconstruction with target multi-view imaging cell scattering intensity (ICSI) fluctuation, the traditional compressed sensing (CS) algorithms suffer from undesirable imaging quality and high computational complexity. This paper proposes a complexvalued deep unrolling network for RCI based on the fast iterative shrinkage-thresholding algorithm (FISTA), called RCI-DUNet. We first convert the complex-valued form of the radar signal to realvalued form using the complex-to-real arithmetic rule, and then propose a momentum correction module (MCM) with an acceleration function that achieves stable gradient propagation while maintaining phase coherence. We design a sparse transformation function based on convolutional neural network (CNN) and channel attention (CA) mechanism to dynamically adapt to target multiview ICSI fluctuation, effectively solving the problem of correlation mismatch between reference signals and echoes. We apply RCIDUNet to multipe input multiple output (MIMO) RCI of target multi-view ICSI fluctuation. It is shown that RCI-DUNet can provide high-quality imaging results even at low sampling rates with drastic ICSI fluctuation. Moreover, RCI-DUNet can greatly reduce imaging time compared to traditional CS algorithms, which can be used for fast RCI. The simulation results validate that RCI-DUNet achieves excellent reconstruction performance and high efficiency in MIMO RCI with target multi-view ICSI fluctuation.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".