A Group Target Data Association Algorithm Based On D-S evidence theory
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
It is difficult to track every target steadily and effectively when the interval of obtaining data from the spaceborne sensor is relatively long.Consequently,a scheme is developed which regards the group targets as the study object and this paper presents a group target data association algorithm based on D-S evidence theory that takes full advantage of their relatively stable composition and array features.Firstly,the composition feature and the array feature of group targets are extracted based on their mathematics model which is established.Secondly,the association matching model of group targets is established that makes use of the composition features and the array features.The basic probability assignment function of the composition features and the array features are calculated respectively based on D-S evidence theory.And finally,the synthetical basic probability assignment function is established after the two basic probability assignment functions are synthesized based on D-S evidence theory and the association result is obtained after making use of the 2D assignment algorithm.The simulation has proved the effectiveness of the algorithm.
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.
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 | 0.002 | 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 | 0.001 |
| Open science | 0.000 | 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 it