WHY THE HORIZON IS IMPORTANT FOR AIRBORNE SENSE AND AVOID APPLICATIONS
Bibliographic record
Abstract
Abstract. The utility of the horizon for airborne sense-and-avoid (ABSAA) applications is explored in this work. The horizon is a feature boundary across which an airborne scene can be separated into surface and sky and serves as a salient, heading-independent feature that may be mapped into an electro-optical sensor. The virtual horizon as established in this paper represents the horizon that would be seen assuming a featureless earth model and infinite visibility and is distinct from the apparent horizon in an imaging sensor or the pilot’s eye. For level flight, non-maneuvering collision course trajectories, it is expected that targets of interest will appear in close proximity to this virtual horizon. This paper presents a model for establishing the virtual horizon and its projection into a camera reference plane as part of the sensing element in an ABSAA system. Evaluation of the model was performed on a benchmark dataset of airborne collision geometries flown at the National Research Council (NRC) using the Cerberus camera array. The model was compared against ground truth flight test data collected using high accuracy inertial navigation systems aboard aircraft on several ’near-miss’ intercepts. The paper establishes the concept of ’virtual horizon proximity’ (VHP), the minimum distance from a detected target and the virtual horizon, and investigates the utility of using this metric as a means of rejecting false positive detections, and increasing range at first detection through the use of a region of interest (ROI) mask centred on the virtual horizon. The use of this horizon-centred ROI was shown to increase the range at first detection by an average factor of two, and was shown to reduce false positives for six popular feature detector algorithms applied across the suite of flight test imagery.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".