GP-PDA Filter for Extended Target Tracking With Measurement Origin Uncertainty
Bibliographic record
Abstract
Extended target tracking (ETT) is an issue in high-resolution radar surveillance, ship tracking, and video tracking. Most of the previous works focus on tracking an ellipsoidal extended target without measurement origin uncertainty (missed detections and clutter). In this paper, a new estimator called the Gaussian process probabilistic data association (GP-PDA) filter is proposed to track an irregularly shaped extended target with measurement origin uncertainty. First, a generalized measurement model for ETT using the Gaussian process (GP) is presented. Both the interior scattering points and the external clutter are considered in this model. Second, a GP-based gating technique is constructed to select validated measurements to feed the filter. Third, the GP-PDA filter is proposed to simultaneously estimate the kinematic state and the contour state of the extended target with measurement origin uncertainty. It is proven that the GP-PDA is a generalized version of the classic PDA, and the latter is a special case of the former in the point target tracking applications. Finally, the GP-based posterior Cramér-Rao lower bound (PCRLB) is derived to evaluate the performance of the ETT with measurement origin uncertainty. Two cases of the PCRLB are discussed, with the number of scattering points being known and unknown. Simulation results verify the effectiveness of the proposed method.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".