<title>Implementation of 1D/2D/3D sensor data fusion in CASE_ATTI test-bed</title>
Bibliographic record
Abstract
Fusion problem of dissimilar sensor data in the CASE_ATTI test-bed is considered. The sensors suite simulated includes an ESM sensor that reports bearing-only contacts, a 2D radar that reports range-bearing contacts, an IRST sensor that reports bearing-elevation contacts, and a 3D radar that reports full 3D contacts. To fuse all this information, CASE_ATTI is modified into a two-layer fusion architecture, with four sensor-level trackers and a central fusion node. Therefore, the fusion of all the dissimilar 1D, 2D and 3D tracks represents an important problem that this paper addresses. The important and directly related issue of tracking with angle-only reports is also addressed. The angle-only tracking represents an important issue in modern surveillance systems and has been extensively studied in recent years. Angle-only tracking systems are known to be unobservable unless the interceptor over-maneuvers the target. A divergence of the target state estimate may occur in the case of stationary or non-maneuvering interceptor. In this article, a new time alignment algorithm, that enhances stability, even for non-maneuvering interceptors, is developed. The proposed algorithm is based upon the modified spherical coordinate representation, but uses a different discretization approach that leads to a more stable behavior. Comparative scenario that illustrates the efficiency of the proposed architecture is presented.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".