An assignment based algorithm for multiple target localization problems using widely-separated MIMO radars
Why this work is in the frame
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Bibliographic record
Abstract
Multiple-Input Multiple-Output (MIMO) radars with widely-separated antennas have attracted much attention in recent literature. The highly efficient performance of widely-separated MIMO radars in target detection compared to multistatic radars have been widely studied by researchers. However, multiple target localization by the enlightened structure has not been sufficiently explored. While Multiple Hypothesis Tracking (MHT) based methods have been previously applied for target localization, in this paper, the well-known 2-D assignment method is used instead in order to handle the computational cost of MHT. The assignment based algorithm works in a signal-level mode. That is, signals in receivers are first matched to different transmitters and, then, outputs of matched filters are used to find the cost of each combination in the 2-D assignment method. The main benefit of 2-D assignment is to easily incorporate new targets that are suitable for targets with multiple scatters where a target may be otherwise unobservable in some pairs. Simulation results justify the capability of 2-D assignment method in tackling multiple target localization problems, even in relatively low SNRs.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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