Synthetic vision for rotorcraft: low level flight
Bibliographic record
Abstract
Two topics are discussed in this paper. The first is the Integrated Multi-sensor Synthetic Imagery System (IMSIS), being developed under an Army SBIR contract. The system updates on-board, pre-stored, terrain elevation data with 3D terrain elevation sensor data (such as radar). The system also merges 2D image contrast sensor data (such as infrared imagery) with the updated 3D terrain elevation data to render a synthetic image of the terrain on the rotorcraft pilot's display. The second topic is the testing of a new flight path marker, to show the pilot the predicted location of the aircraft with respect to the synthetic terrain (at 100m distance), as well as the predicted height above the terrain, the desired height above the terrain, and the point on the terrain the aircraft is expected to fly over. The Altitude and ground Track Predicting Flight Path Marker (ATP-FPM) symbol takes advantage of knowledge of terrain elevations ahead of the aircraft from a synthetic vision system, such as IMSIS. In simulation, the maximum low altitude error and maximum ground track error were both reduced by a factor of 2 with the ATP-FPM compared to the traditional instantaneous flight path marker. Pilot-to-pilot variations in performance were reduced and workload decreased with the ATP-FPM.
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.
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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".