Advancements in Jammer Location Identification and Suppression: Employing a Multi-Target Least Square Constant Modulus Array Approach
Bibliographic record
Abstract
In the domain of array signal processing, the identification and suppression of jamming signals pose significant challenges, particularly in scenarios where intentional interferers operate in the far-field region.This study introduces an innovative beamforming technique, the multi-target least square constant modulus algorithm (MT-LSCMA), which surpasses traditional direction-of-arrival (DOA) estimation methods like estimation of signal parameters via rotational invariant techniques (ESPRIT) and multiple signal classification (MUSIC) by addressing their limitations in computational complexity, detection efficacy, and inaccuracies arising from coherent sources.Unlike conventional approaches, the MT-LSCMA, an extension of the blind constant modulus adaptive beamforming method, does not rely on a reference signal for the optimization of the mean-square-error (MSE) cost function.Instead, it iteratively updates the weights based on constant modulus signal information, facilitating the identification of jammer locations even under low signal-tonoise ratios (SNR).This methodology enhances anti-jamming capabilities by adaptively forming nulls in the radiation pattern directed towards the jammers.Simulation results demonstrate the superior accuracy of the MT-LSCMA in tracking jammers compared to both traditional and recently developed techniques.The proposed method yields significant improvements in detection probability, resolution probability, failure rate, computational complexity, and root-mean-square-error (RMSE), thus offering a robust solution for effective jammer location identification and suppression.
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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 | 0.000 |
| 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) | 0.001 | 0.001 |
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".