Comparison of the unscented and cubature Kalman filters for radar tracking applications
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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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 | 0.012 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".