Experimental Evaluation of PICAS: An Electro-Optical Array for Non-Cooperative Collision Sensing on Unmanned Aircraft Systems
Bibliographic record
Abstract
This paper describes the initial flight test evaluation of the Passive Intelligent Collision Avoidance Sensor (PICAS) developed at the National Research Council of Canada (NRC). PICAS represents the latest iteration of a non-cooperative electro-optical (EO) airborne collision sensing instrument designed to explore technology appropriate for under-25 kg Unmanned Aircraft Systems (UAS). PICAS is a prototype, selectively-sampled, multi- camera array mated to a computing platform capable of simultaneously recording and processing images in real-time. A selective sampling approach tailored the sensor to the performance requirement by varying the angular resolution and field of view as a function of azimuth. PICAS was designed to detect a Cessna 172-sized target at 10 km in the head- on direction. The sensor was flight-tested on a Bell 205 rotorcraft acting as a surrogate UAS and flying collision-course intercepts against a Harvard Mark IV intruder. An NRC developed Collision Intercept Display was utilized to provide beyond visual line of sight guidance for both aircraft to conduct the intercepts. Once the collision geometry was coordinated, the Bell 205 switched to automatic operation, controlling altitude, ground- speed and ground-track for the duration of the run. PICAS was operated in pure recording mode, with each camera recording synchronized and time-stamped images at 15 frames per second. The detection performance was evaluated by simulating the real-time processing algorithms against pre-collected imagery and associated aircraft data. Analysis results indicated that PICAS exceeded the minimum detection requirement throughout its field of view for an under-25 kg UAS operating at an airspeed of 80 knots encountering typical (Cessna 172) intruders on co-altitude collision course geometries in Canadian Class G airspace.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".