Adaptive Beam Scheduling for Cooperative Phased Array Radars With High-Precision Pencil-Beam
Bibliographic record
Abstract
Phased array radar (PAR) has attracted considerable attention in civil and military applications due to its capability of performing multiple tasks such as surveillance, tracking and weapon engagement simultaneously. To make better use of limited radar resources and to offer best operating performance, an efficient resource allocation strategy is necessary. Pencil-beams with super narrow beamwidth is prospective to resource-aware design but using them to cover areas of interest especially in cases of maneuvering targets with high motion uncertainty requires more study. Existing works often assume that a beam can cover the entire area of interest and the problem of scheduling small-beamwidth pencilbeam to perform search and track (SAT) efficiently is barely discussed or addressed in literature. In this paper, the problems of tracking with pencil-beam and its beam scheduling optimization are addressed. Three beam scheduling strategies, fixed linear wipe, open-loop linear wipe that uses hierarchical genetic algorithm (HGA), and expected posterior Cramér–Rao lower bound (EPCRLB) based optimal solution, are proposed to solve the mixed integer nonlinear problem (MINP). To handle the partially covered target existence area by pencil-beam, a new concept of predicted expected posterior Cramér–Rao lower bound (P-EPCRLB) is proposed and used as the main optimization criterion for the scheduling strategy. Numerical results demonstrate the superior performance of the proposed EPCRLB based optimal solution strategy and its effectiveness as a proposed solution.
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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) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".