3-D Pseudo-Noise Interferometry for a Novel Bistatic Aperture Lidar
Bibliographic record
Abstract
We reported an unconventional coherent Pseudo-Noise (PN) interferometry with true 3-D lidar imaging capability by recording multiple fast-time acquisition for parallel 2-D ranging while achieving azimuth compression for the third dimension without using a filled aperture. Two transmitters broadcast the same but mutually time-delayed Binary Phase Shift Keyed (BPSK) PN-code riding on shifted RF carrier frequencies to track the quadratic phase trajectory of the target in azimuth using synthetic wavelength. The range/cross-range is obtained while two time-delayed backscattered BPSK signals interfere at the receiver through code-multiplexing of the interferometric PN-modulation that encodes time-difference-of-arrival (TDOA) and time-of-flight (TOF) delay in parallel using the shift-and-add property of the maximum-length (ML) sequences. The simulation and proof-of-concept experiment demonstrate synthetic aperture (SA) imaging in azimuth dimension in addition to the 2-D parallel ranging from the PN interferometry.
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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 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".