Army Aviation Fusion of Sensor-Pushed and Agent-Pulled Information
Bibliographic record
Abstract
Supporting the Aviation Applied Technology DirectorateOs (AATD) Rotorcraft PilotOs Associate Advanced Technology Demonstration Program, the Lockheed Martin Advanced Technology Laboratories (ATL) demonstrated that formation of a Common Tactical Picture (CTP) from onboard and offboard sensors, via data fusion, was essential to automated decision aiding. In this case, the CTP was formed solely by the fusion of data provided by sensors dedicated to ownship tasks and from offboard sensors whose data was received via onboard processing systems, like JTIDS and the Improved Data Modem. Future network connectivity of aviation decision aiding systems to Army Battle Command System elements poses an opportunity to significantly enhance sensor data-only CTPs, if capability to autonomously and persistently discover and retrieve information from these stovepipe systems is applied. Addressing this challenge, the Lockheed Martin Advanced Technology Laboratories (ATL) has leveraged two autonomy-enabling technologies Ð multi-sensor data fusion and mobile intelligent agents, for Army aviation fusion of sensor-pushed and agent-pulled information. Over $14M in contracts and ATL research and development was combined to demonstrate these technologies in an Army ACT II proof-of-concept demonstration at the Air Maneuver Battle Lab. ATL plans to extend this concept for the AATD Airborne Manned and Unmanned System Technology Science and Technology Objective and the AATD Hunter Standoff Killer Team Advanced Concept Technology Demonstration.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".