Optimal Vehicle-Target Assignment: A Swarm of Pursuers to Intercept Maneuvering Evaders Based on Ideal Proportional Navigation
Bibliographic record
Abstract
The problem of vehicle-target assignment (VTA) to capture a team of evading targets using a swarm of pursuing vehicles is investigated in this article. The VTA problem is formulated as an integer linear programming (ILP), such that the time to intercept all the targets is minimized subject to a number of constraints. To obtain closed-form formulas for the time-to-go matrix in the framework of ILP optimization, a one-on-one pursuit-evasion problem based on the ideal proportional navigation (IPN) guidance law is investigated. By considering two different scenarios of non-maneuvering and maneuvering evaders, analytical closed-form solutions for the pursuit-evasion time-to-go as explicit functions of the position and velocity vectors of the pursuers and evaders are developed, and efficient evasion strategies based on IPN guidance scheme are presented. The efficacy of the theoretical results in estimating the elements of time-to-go matrix is demonstrated by solving the VTA problem in simulations.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".