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.
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.
How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".