A Microwave Photonic Radar Warning Receiver based on Deep Compressed Sensing
Bibliographic record
Abstract
A wideband photonics-based radar warning receiver that exploits a novel deep-neural-network-enabled compressed sensing technique is proposed and experimentally demonstrated. In the scheme, first, the radar input signal is mixed with a pseudo-random-sequence electrical signal using a wideband microwave photonic mixing approach; then, the mixed signal is filtered with a low-pass electrical filter. The resulting compressed signal is digitized using a low-speed analog-to-digital converter. Instead of relying on a computationally expensive compressed sensing waveform-recovery algorithm, followed by conventional parameter-extraction processing, a deep neural network algorithm is used to rapidly estimate the radar waveform parameters directly from the low-frequency compressed signal. A proof-of-concept receiver is tested in a laboratory experiment using pulsed radar signals with different center frequency, bandwidth, and pulse repetition frequency: it reaches an analog input bandwidth of 5 GHz, limited by the available laboratory equipment, a compression factor of 20 (i.e., the digitized signal has frequency content in the 0-250 MHz range), very accurate estimation of the radar waveform parameters, and processing time of a few milliseconds.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".