Improved target tracking using kinematic measurements and target orientation information
Bibliographic record
Abstract
This paper discusses a target tracking system that provides improved estimates of target states using target orientation information in addition to standard kinematic measurements. The objective is to improve state estimation of highly maneuverable targets with noisy kinematic measurements. One limiting factor in obtaining accurate state estimates of highly maneuvering targets is the high level of uncertainty in velocity and acceleration. The target orientation information is helpful in alleviating this problem to accurately determine the velocity and acceleration components. However, there is no sensor that explicitly measures target orientation. In this paper, the Observable Operator Model (OOM) is used together with multiple sensor information to estimate target orientation measurement. This is done by processing the sensor feature measurements from different aspect angles and the estimated target orientation measurement is used in conjunction with kinematic measurements to conclusively estimate target states. Simulation results show that the incorporation of target orientation can enhance the tracking performance in the presence of fast moving and/or maneuvering targets. In addition, the Posterior Cramer-Rao lower bound (PCRLB) that quantifies the achievable performance is derived. It is shown that the proposed estimator meets the PCRLB.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".