Fourier‐Bessel transform and time–frequency‐based approach for detecting manoeuvring air target in sea‐clutter
Bibliographic record
Abstract
In many applications, it may be desired to decompose a non‐stationary signal into its individual components. If spectral components of the non‐stationary signal do not overlap in the frequency domain then Fourier transform can be used to decompose the non‐stationary signal. Fourier transform fails to decompose the non‐stationary signal if its spectral components overlap in the frequency domain. In this study, the authors propose Fourier‐Bessel transform and the time–frequency analysis in conjunction with the fractional Fourier transform (FB‐TF) method for the separation of multi‐component non‐stationary signal whose components overlap in both time and/or frequency domains. The efficiency of the proposed method is compared with one of the traditional decomposition methods like EMD. The proposed approach is applied to both simulated and experimental radar data. Results demonstrate the effectiveness of the proposed method for non‐stationary signal separation and for detecting manoeuvring target in heavy sea‐clutter environments. The improvement factor and clutter attenuation are calculated and used to compare the performance of the EMD and the FB‐TF methods in suppressing the sea‐clutter and enhancing target detection. The proposed method can be used as a potential tool for detecting and enhancing the low observable manoeuvring air targets in the sea‐clutter environment.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".