Receding Horizon Model-Based Predictive Control for Dynamic Target Tracking: a Comparative Study
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Bibliographic record
Abstract
when the targets are moving. This article describes an application of receding horizon model-based predictive control for UAV target tracking in a dynamic environment. The advantage that predictive control oers over traditional control strategies is that it can account for predicted/future UAV and target trajectories. As such, the proposed method is compared to ve existing target tracking algorithms to demonstrate its eectiveness. For each tracking algorithm, simulations are conducted to test the UAV’s aptitude to track a moving ground target and performance metrics, namely the mean and maximum UAV-to-target distance for the simulation duration, are used to evaluate each method. Simulation results demonstrate that the proposed UAV guidance strategy based on predictive control improves the aircraft’s ability to track a dynamic ground target.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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