Enhanced Meta-Heuristic Approach for Echo Suppression in Synthetic Aperture Radar Using Non-Periodic Interrupted Sampling
Bibliographic record
Abstract
Echo cancellation is a deception jamming technique designed to nullify the target's echo and prevent its detection by enemy radar.Traditional methods rely on time-domain synchronization, but they struggle to fully suppress echoes when there is an amplitude mismatch.This limitation often leaves the target partially visible in synthetic aperture radar (SAR) images, reducing the effectiveness of the jamming technique.Non-periodic interrupted sampling modulation (NP-ISM) is applied as it produces a continuous jamming strip instead of discrete false targets whereas in Periodic sampling multiple false targets emerge even the real target echo is canceled which makes it easy to be countered by enemy radar.The improved henry gas solubility optimization algorithm (IHGSOA) is applied in the echo cancellation model to optimize the random rectangular envelope pulse train in the NP-ISM which enhances signal cancellation.This significantly reduces the Peak to Average Ratio (PAR), enabling more effective target echo suppression.A polynomial non-linear frequency modulation (PNLFM) is employed to reduce the PAR leading to significant improvement in signal-to-noise ratio (SNR), enabling more effective target echo cancellation.The proposed IHGSOA-based echo cancellation model effectively aligns the cancellation and echo signals through time-delay synchronization, minimizing errors even in the presence of a mismatch.The simulation results show that using the above cancelling system the desired target echo amplitude drops to 50% and 40% of its original target echo, hence the system has a very high cancellation effect.Thus, the effectiveness of the IHGSOA is evaluated by comparing its performance with other conventional algorithms.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".