Joint detection and tracking of unresolved targets with a monopulse radar using a particle filter
Bibliographic record
Abstract
Detection and estimation of multiple unresolved targets with a monopulse radar is a challenging problem. For ideal single bin processing, it was shown in the literature that at most two unresolved targets can be extracted from the complex matched filter output signal. In this paper, a new algorithm is developed to jointly detect and track more than two targets from a single detected bin. This method involves the use of tracking data in detection. For this purpose, target states are transformed into detection parameter space, which involves high nonlinearity. In order to handle this, the sequential Monte Carlo (SMC) method, which is proved to be effective for nonlinear non-Gaussian estimation problems, is used as the basis of the closed loop system for tracking multiple unresolved targets. In addition to the standard SMC steps, the detection parameters corresponding to the predicted particles are evaluated using the nonlinear monopulse radar beam model. It in turn enables the evaluation of the likelihood of the monopulse signal given tracking data. That is, we evaluate the likelihoods of different hypotheses of possible combinations of targets being in different detected bins. The hypothesis testing is used to find the correct detection event. The particles are updated and resampled according to the hypothesis that has the highest likelihood (score). A simulated amplitude comparison monopulse radar is used to generate the data with more than unresolved two targets. Simulation results confirm the possible extraction and tracking of more than two targets jointly.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".