Meta level tracking with multimode space-time adaptive processing of GMTI data
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Ground surveillance of the battlefield provides military analysts with information that is critical to the success of a mission; the type of the information includes the enemy force structure, enemy offensive combat formation, and maneuvering events. The conventional approach uses mainly the synthetic aperture radar (SAR) and electro-optical (EO) sensors to perform detection and identification of stationary targets on the battlefield. Ground moving target indicator (GMTI) radar with space-time adaptive processing (STAP), on the other hand, allows a more complete perception of the battlefield by adding the capability to detect moving objects over a large area. In particular, the simultaneous detection and estimation of angular location of a ground moving target via adaptive cancellation of ground clutter is demonstrated, where a single reflector antenna with a multimode feedhorn is used in a GMTI radar. Based on the GMTI radar output, we illustrate the use of stochastic parsing algorithm with stochastic context free grammar (SCFG) as an unifying framework for data association, target tracking, and situation awareness.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Open science | 0.002 | 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 it