Estimating Resonances in Low-SNR Late-Time Radar Returns With Sampling Jitter
Bibliographic record
Abstract
The frequency and attenuation rate of a resonance in the late-time return of a radar signal are indicative of a target's geometry and conductivity, and hence they can be used as features in a variety of filtering and classification applications. However, late-time returns are typically observed over short windows at low signal-to-noise ratios (SNRs, averaged over the window), and often in the presence of sampling jitter. This can make the estimation of these parameters difficult, even when multiple measurement shots are available. In this article, we develop a new multi-shot estimation method that is based on models for the distribution of the roots of the z-transform of the received signal. Under an additive-Gaussian-noise model, we have a closed-form expression for the root distribution in terms of the resonance parameters, and the parameters are estimated by matching the model distribution to the empirical distribution. The root distribution has a strong dependence on the frequency and attenuation rate, and leads to significantly better estimates than existing techniques at low SNRs. By developing approximate models, we extend these performance advantages to scenarios with significant sampling jitter and synchronization offsets.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".