Joint detection and tracking of unresolved targets with a joint-bin processing monopulse radar
Bibliographic record
Abstract
Detection and estimation of multiple unresolved targets with a monopulse radar is limited by the availability of information in monopulse signals. The maximum possible number of targets that can be extracted from the monopulse signals of a single bin is two. Recently two approaches have been proposed in the literature to overcome this limitation. The first is joint-bin processing that exploits target spill-over among adjacent cells by modeling the target returns in the adjacent cells. In addition to making use of the additional information available in target spill-over, it handles a more practical problem where the usual assumption of ideal sampling is relaxed. The second approach is to make use of tracking information in detection through joint detection and tracking with the help of Monte Carlo integration of a particle filter. It was shown that the extraction of even more targets is possible with tracking information. In this paper, a new approach is proposed to combine make the best of these two approaches - a new joint detection and tracking algorithm with multibin processing. The proposed method increases the detection ability as well as tracking accuracy. Simulation studies are carried out with amplitude comparison monopulse radar for an unresolved target scenario. The relative performances of various methods are also provided.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".