Multi‐Shot Estimation of Resonance Parameters of Late‐Time Radar Returns in Clutter
Bibliographic record
Abstract
ABSTRACT The resonance parameters of late‐time returns (LTRs) can be used as features in the identification of radar targets. However, reliable estimation of the complex frequency of each resonance is notoriously difficult. This is a result of the short duration of the LTR, its low effective signal‐to‐noise ratio (SNR) and the inherent sensitivity of the estimation problem. These issues are exacerbated when the radar background includes resonating clutter. We develop an effective technique for estimation the complex frequencies of a target's resonances for scenarios in which the radar can obtain multiple measurement shots of the background (clutter) alone and multiple measurement shots of the target in the presence of the background. The proposed method exploits the fact that the maximum likelihood estimator for measurements in Gaussian noise can be decomposed to estimate the complex frequencies of the resonances separately from their complex amplitudes. This enables us to decouple the estimation of the complex frequencies of the target from those of the background because the background's complex frequencies remain largely unchanged when the target is introduced. We investigate the performance of the proposed method using a radar that operates in the band of 0.5–5 GHz and employs equivalent sampling at a rate of 20 GSa/s. Proof‐of‐concept experiments on brass rods of known length validate the overall approach, and experiments on more complex targets in clutter demonstrate its potential for practical applications.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".