Design of Pulse Characteristics for Near-Field UWB-SAR Imaging
Bibliographic record
Abstract
In this paper, the design of pulse characteristics to achieve the desired image resolution for near-field synthetic aperture radar is presented. Gaussian and chirp pulses, which are the most commonly used pulses for ultra-wideband (UWB) radar applications, are considered in this paper. The effect of the pulse shape, bandwidth, integration angle, and signal-to-noise ratio (SNR) of the received pulse on the image resolution is comprehensively studied. To enhance the image resolution, preprocessing of the received pulses with envelope detection or match filtering are also studied. The range and cross-range resolutions achieved by Gaussian and chirp pulses with the same center frequency and bandwidth at various SNR values are compared. This paper shows that the Gaussian pulse with envelope detection provides better image resolution, whereas the chirp pulse with match filtering provides more resistance to noise. Closed-form equations and design guidelines are developed to design the input pulse characteristics to achieve the desired image resolution. The antennas' effect on UWB pulses and the developed equation for cross-range resolution, are both validated using full-wave simulations and measurements.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".