Vision-Based Geolocation of Moving Ground Targets Using Kalman Filtering with a Gimbal Camera on Board a UAV
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Bibliographic record
Abstract
Unmanned aerial vehicles (UAVs) are vital for surveillance missions requiring the geolocation of moving ground targets, yet small, resource-constrained platforms often lack integrated, robust systems that can handle disturbances such as wind, occlusions, and noise. This paper presents an integrated, end-to-end vision-based geolocation pipeline specifically designed for embedded deployment on resource-constrained UAVs with gimbal cameras. Starting from a rough initial position estimate, pan/tilt angles are computed to orient the gimbal, and then a visual tracking module combining object detection (via Tiny-YOLO) and feedback control (using CSRT) centers the target in the frame. The target’s absolute position is derived from UAV inertial data and gimbal angles. To mitigate noisy or unavailable direct geolocation due to disturbances or visual lock loss, Kalman filtering is integrated with a unicycle-based motion model. Both an extended Kalman filter (EKF) and unscented Kalman filter (UKF) are evaluated and tuned in high-fidelity simulations, with the UKF demonstrating superior performance by reducing the 2D position RMSE by 33% compared to the EKF in occlusion scenarios. The system is implemented on embedded hardware and validated through real flight tests, establishing the operational capability of vision-based surveillance on small UAV platforms.
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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 | 0.000 | 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 | 0.000 |
| 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