Coherent Radar Target Detection With In-Band Cyclostationary Wireless Interference
Bibliographic record
Abstract
Spectral congestion necessitates the in-band operation or the spectrum-sharing of legacy radar and communication systems. Since these systems operate in the same band in spectrum-sharing mode, they interfere with one another. To address this problem from the radar’s perspective, this paper considers the coherent detection of target-reflected radar signals in the presence of interference from an in-band cyclostationary digital modulated wireless communication signal. Three different cases of target-reflected radar signals, namely, deterministic signals, signals with random phase, and completely random signals, are considered in this paper. The optimum detection rules are derived for these three cases and the corresponding receiver structures for the equalization of the interfering signal are presented. Sub-optimum detection structures are also derived with the assumption that the in-band interference is a white stationary time-invariant Gaussian process. Further, considering the equalization, modified CFAR receiver structures are also presented. By considering the mathematical models for cyclostationary or periodic in-band interference, the performances of the optimum, sub-optimum detectors, and modified CFAR detectors are quantified analytically in terms of detection probability and false alarm probability, and the resulting receiver operating characteristic (ROC) curves are analyzed as a function of the signal-to-interference ratio. It is demonstrated that improper equalization of the interfering signal significantly affects the performance of the optimum detector and this impact is analyzed in detail. As spectrum-sharing becomes more prevalent due to spectrum congestion, the proposed optimal, sub-optimal, and modified CFAR detection rules and receiver structures can be incorporated into existing systems with substantial savings.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".