Detection Performance using Frequency Diversity with Distributed Sensors
Why this work is in the frame
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Bibliographic record
Abstract
Detection using a frequency diverse (FD), distributed, radar system is investigated. Distributed sensing systems provide an inherent spatial diversity by viewing a potential target from different aspect angles. By using different frequencies at each platform, a diversity gain is obtained in addition to the advantages of spatial diversity while also avoiding mutual interference. Here, since platforms are distributed spatially, true time delay is used at each platform to align the sample look point in time. Data models for a distributed system with and without frequency diversity are developed. These models are used to analyze the corresponding signal-to-interference-plus-noise ratio (SINR) and probability of detection for the two cases in the context of space-time adaptive processing (STAP). The simulation results presented here illustrate the limitations imposed by mutual interference and the significant benefits of spatial and frequency diversity.
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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