Nonlinearity-Compensated Short-Range FMCW Radar for Weak Target Imaging
Bibliographic record
Abstract
An easily implementable, low-cost, and portable broadband frequency-modulated continuous wave (FMCW) synthetic aperture radar (SAR) system is presented for short-range imaging applications. Design considerations and radar performance metrics are discussed in detail while investigating systematic and nonsystematic performance-limiting factors. This article introduces a signal processing procedure based on a closed-form comprehensive mathematical model to characterize the impact of the nonlinear frequency sweep on radar performance. Based on the proposed model, the nonlinearity is compensated using the time-resampling technique and without needing a reference response. A single-zone calibration does not provide enough accuracy when multiple targets are widely distributed in the cross-range direction. A range- and angle-dependent calibration scheme is proposed to mitigate the second-order effects more precisely, which are otherwise difficult to model mathematically. Detecting less reflective targets in the presence of a strong scatterer is challenging. A range-gating method based on a tunable active bandpass filter (BPF) is proposed to improve the radar system’s sensitivity by suppressing the dominant reflection and enhancing the weaker scatterer. The results are verified by developing SAR images of targets in free space and through a residential wall.
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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 | 0.000 |
| 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 | 0.000 |
| 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".