Comparison of filtering algorithms for ground target tracking using space-based GMTI radar
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
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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