Deep Learning-Based Interference Detection, Classification, and Forecasting Algorithm for ESM Radar Systems
Bibliographic record
Abstract
In this study, aiming to address the challenges posed by interference from communication systems and jammers, we investigate the application of deep learning (DL) in electronic support measures (ESM) radar systems. Our primary objective is to detect, classify, and forecast interference that can disrupt detection of low probability of intercept (LPI) and low probability of detection (LPD) signals. The proposed algorithm uses a time-frequency distribution (TFD) and received interference strength (RIS) to detect and predict interference. To ensure high precision,we develop a new DL-based outlier detection (OD) technique that is based on the relationship between true positive rate (TPR) and latent space. More specifically, the OD technique applies a new dual-threshold mechanism to the TFD representation for interference detection. We also introduce a DL-enabled classifier designed using the OD architecture to identify the source of interference. Finally, we forecast the RIS by proposing a new DL autoregressive (AR) model through a sliding window designed using the classifier’s output. By integrating OD in classifier design and using its output for forecasting, our approach achieves superior accuracy as compared to independent models. Simulation results demonstrate that the proposed algorithm outperforms others, particularly in low signal-to-interference plus noise ratio (SINR) conditions. Specifically, in terms of interference detection, our algorithm achieves 0.9978 TPR, 0.9415 recall, and 0.0004 false positive ratio (FPR). With regard to classification, it records 0.9784 precision and 0.7847 recall. In forecasting, it achieves a 0.2100 mean average error (MAE), thus significantly enhancing ESM radar awareness. The TFD feature also proves to be more accurate than in-phase and quadrature features. These strengths, coupled with an optimal balance of cost and accuracy, make our framework robust and resistant to interference.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".