Accuracy enhancement of underwater target detection with time-frequency analysis techniques
Bibliographic record
Abstract
Underwater target detection is mainly based on acoustic emissions generated by the target of interest (TOI) as it propels itself through the ocean, and operates non-propulsion-related onboard systems. The spectral signature of these acoustic emissions is used for identification and localization of TOI. This paper focuses on underwater target detection using passive sonobuoys. Discrete Fourier transform (DFT) is used in most sonobuoy processing systems to provide spectral analysis of the received signals. Due to the relatively high noise level and the several sources of interference that may exist underwater, the DFT may not determine the spectral signature of TOI with adequate accuracy. The low signal-to-noise ratio (SNR) and the presence of strong interference sources with frequencies close to the TOI frequency jeopardize the detection accuracy, the bearing estimation performance, and target tracking capabilities. The aim of this paper is to: (1) examine the performance of both wavelet packet analysis and high resolution spectral estimation techniques, and (2) provide a comparative study between both methods. Based on underwater acoustic simulation developed in this research, results showed that the proposed methods can achieve robust target detection with low levels of SNR and interferences of nearby signatures that cannot be detected by DFT.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".