A Co-Channel Signal Detector Based on Phase Tracking for Pulse Doppler Radar
Bibliographic record
Abstract
Doppler processing is routinely applied to isolate targets from noise, clutter and interference in conventional pulse-Doppler radar. If a target is co-located in range and azimuth and has a similar radial velocity (co-channel) as another target (or interference), that target may not be detected. This is because only amplitude or power information is used in the detection stage, which does not provide sufficient information to discriminate co-channel signals from each other. In this paper we propose a detector which can resolve co-located targets (or target with interference) with similar Doppler frequencies. Instead of only using phase information for the coherent integration, our proposed detector tracks the phase modulation differences of the co-located, co-channel, targets. The amplitude information of the targets can also be estimated and forwarded to the tracker. One application of this method is HF radar where a target may have a Doppler frequency similar to a Bragg line - first order sea clutter. The Bragg lines are generally dominant at all ranges and exist in all directions. Conventional processing fails to discriminate when targets radar features are similar to the Bragg lines. Simulations of the proposed method show promising results that targets with Doppler frequencies near Bragg lines can be detected.
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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 | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".