Comparison of filtering algorithms for ground target tracking using space-based GMTI radar
Bibliographic record
Abstract
Space-based radar (SBR) systems have received a great deal of attention, since they can provide all-weather, daynight, and continuous world-wide surveillance and tracking of ground, air, and sea-surface targets. The ground moving target indicator (GMTI) mode is an important operating mode for such systems. GMTI radar measurements are the range, azimuth and range-rate, which are nonlinear functions of the target state. We consider the extended Kalman filter (EKF), unscented Kalman filter (UKF), and particle filter (PF) for the SBR GMTI nonlinear filtering problem and present a new track initiation algorithm. We compare the mean square errors (MSEs) and computational times using simulated data generated by Monte Carlo simulations. Although the cross-range errors are large, our results show that the MSEs of the filters are nearly the same. Our results show that the EKF performs the best for the scenario considered based on the MSE and computational time.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| 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".