Time Resource Management in Cognitive Radars Based on Parameter Optimization
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In this study, a cognitive radar system that optimizes radar waveform parameters to increase the accuracy of radar tracking systems and balance time resource management is discussed. In the study, a cost function for the optimization of waveform parameters is proposed with the help of unscented Kalman filter (UKF) and interacting multiple models (IMM) methods. Thus, the tracking performance of targets with various movement types has been increased and the time resource has been used more efficiently. The performance of the proposed system is examined under a target tracking scenario that includes various maneuvers. In the analyzed scenario, the effect of the proposed cost function on the system performance was evaluated through track continuity, track estimation accuracy and time resource consumption. When the results obtained with the help of computer-aided simulations are examined, it is observed that the track update interval and the duration of stay on the target are updated adaptively according to the position and maneuver status of the target, and thus the time resource is consumed more efficiently.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it