Synthetic aperture ladar based on a MOPAW laser
Bibliographic record
Abstract
Long range land surveillance is a critical need in numerous military and civilian security applications, such as threat detection, terrain mapping and disaster prevention. A key technology for land surveillance, synthetic aperture radar (SAR) continues to provide high resolution radar images in all weather conditions from remote distances. State of the art SAR systems based on dual-use satellites are capable of providing ground resolutions of one meter; while their airborne counterparts obtain resolutions of 10 cm. Certain land surveillance applications such as subsidence monitoring, landslide hazard prediction and tactical target tracking could benefit from improved resolution. The ultimate limitation to the achievable resolution of any imaging system is its wavelength. State-of-the-art SAR systems are approaching this limit. The natural extension to improve resolution is to thus decrease the wavelength, i.e. design a synthetic aperture system in a different wavelength regime. One such system offering the potential for vastly improved resolution is Synthetic Aperture Ladar (SAL). This system operates at infrared wavelengths, ten thousand times smaller radar wavelengths. This paper presents a SAL platform based on the INO Master Oscillator with Programmable Amplitude Waveform (MOPAW) laser that has a wavelength sweep of Δλ=1.22 nm, a pulse repetition rate up to 1 kHz and up to 200 μJ per pulse. The results for SAL 2D imagery at a range of 10 m are presented, indicating a reflectance sensibility of 8 %, ground-range and azimuth resolution of 1.7 mm and 0.84 mm respectively.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 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".