<title>Adaptive sensor placement for target tracking in the presence of uncertainties</title>
Why this work is in the frame
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Bibliographic record
Abstract
Recently a general framework for sensor resource management, which has been shown to allow efficient and effective utilization of a multisensor system was introduced in5. The basis of this technique is to use the Posterior Cramer-Rao Lower Bound (PCRLB) to quantify and control the optimal achievable accuracy of target state estimation. In the current paper we extend this framework by addressing the issues of imperfect sensor placement and uncertain sensor movement (e.g., sensor drift). In contrast the previous work considered only the case where the sensor location is known exactly. The crucial consideration is then how these two forms of uncertainty affect the sensor management strategy. If unaccounted for, these uncertainties will render the output of the resource manager useless. We adjust the PCRLB to account for sensor location uncertainty, and we also allow for measurement origin uncertainty (missed target originated detections and false alarms). The work is motivated by the problem of tracking a submarine by adaptively deploying sonobuoys from a helicopter. Simulation results are presented to show the advantages of accounting for sensor location uncertainty within this focal domain of anti-submarine warfare. The same technique can be used for tracking using unattended ground sensors (UGS) or unmanned aerial vehicles (UAV).
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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 | 0.001 |
| 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