EKF and UKF localization of a moving RF ground target using a flying vehicle
Bibliographic record
Abstract
A search and localization algorithm is used to localize a ground based moving target using an Autonomous Air Vehicle (AAV). It is assumed that the target has RF emissions. AAV utilizes Global Search (GS), Approach Target (AT), Locate Target (LT) and Target Reacquisition (TR) modes to search the entire parts of a desired area, approach the detected target, locate it, and even find the target that stop transmitting RF emissions during the localization process. In GS mode, the AAV searches the area to receive signal from a target. In LT mode, the AAV performs a circular motion around the target and uses non-linear Kaiman filters (Extended Kaiman Filter (EKF) and Unscented Kaiman Filter (UKF)) to estimate the target position. The numerical results obtained from localization by UKF and EKF show that in many cases UKF converges faster. Additionally, in certain noisy conditions only UKF converges.
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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 | 0.000 | 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 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".