Comparison of the unscented and cubature Kalman filters for radar tracking applications
Why this work is in the frame
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Bibliographic record
Abstract
Among the proposed nonlinear filtering algorithms, the unscented Kalman filter (UKF) has been recommended as a better choice than other algorithms for many applications. Recently, the cubature Kalman filter (CKF) was proposed, which was claimed to be even better. This study compares the two algorithms for two radar tracking applications, namely, high frequency surface wave radar (HFSWR) and passive coherent location (PCL) radar. Monte Carlo simulations are used to fulfill the purpose. It is shown that the UKF outperforms the CKF in both radar applications, using performance measures of root mean square error (RMSE) and normalized estimation error squared (NEES). Results show that the PCL radar's higher nonlinearity provides a challenge for the design of nonlinear filters, and that the CKF is not as well suited as UKF to highly nonlinear systems such as PCL. Sensitivity of the filters becomes a critical design issue. (5 pages)
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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