<title>Hybrid radar signal fusion for unresolved target detection</title>
Bibliographic record
Abstract
Radar systems have good radial resolution, but they have poor angular resolution that results in unresolved measurements. This problem can be mitigated by utilizing the spatial diversity of multistatic radar system. In this paper, the detection of unresolved targets with a hybrid radar system using signal level fusion is considered. The system consists of two receivers: one is co-located with the transmitter and the other is located far from the transmitter. The area of interest, where the transmitter is focused on, is divided into grids, which are formed by circular range bins of the monostatic receiver and elliptical range bins of the bistatic receiver. Assuming these grids are good enough to resolve the targets (i.e., each grid has at most one target and vice versa), the amplitudes of the targets (corresponding to all grids) that maximize the likelihoods of the signals obtained from both receivers are determined. These optimum values are then compared against a threshold for the final decision. Simulation studies are performed to demonstrate the proposed algorithm for hybrid radar system with unresolved targets. The simulation results confirm the enhancement in detection of unresolved targets by fusing coherently received signals from both monostatic and bistatic receivers.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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 source (direct Gemma or distilled Codex), 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".