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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".