Dynamic sector processing using 2D assignment for rotating radars
Bibliographic record
Abstract
Electronically scanned array radars as well as mechanically steered rotating antennas return measurements with different time stamps during the same scan while sweeping form one region to another. Data association algorithms process the measurements at the end of the scan in order to satisfy the common one measurement per track assumption. Data processing at the end of a full scan resulted in delayed target state update. This issue becomes more apparent while tracking fast moving targets with low scan rate sensors. In this paper, we present new dynamic sector processing algorithm using 2D assignment for continuously scanning radars. A complete scan can be divided into sectors, which could be as small as a single detection, depending on the scanning rate and sparsity of targets. Data association followed by filtering and target state update is done dynamically while sweeping from one end to another. Along with the benefit of immediate track updates, continuous tracking results in challenges such as multiple targets spanning multiple sectors and targets crossing consecutive sectors. Also, associations performed in the current sector may require changes in association done in previous sectors. Such difficulties are resolved by the proposed 2D assignment algorithm that implements an incremental Hungarian assignment technique. The algorithm offers flexibility with respect to assignment variables for fusing of measurements received in consecutive sectors. Furthermore the proposed technique can be extended to multiframe assignment for jointly processing data from multiple scanning radars. Experimental results based on rotating radars are presented.
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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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".