Optimal Threshold Policies for Hard-Kill of Enemy Radars With High Speed Anti-Radiation Missiles (HARMS)
Bibliographic record
Abstract
In modern network centric warfare (NCW) there is a dedicated platform (airplane) assigned to every group of aircraft that specializes in the hard-kill of the enemy guidance-radars by deploying high speed anti-radiation missiles (HARM)s. In this paper we consider the problem of optimal launch control of the HARMs. We formulate the optimal trade-off between the cost of the HARMs and the latency in performing the hard-kill of the enemy radar as a partially observable Markov decision process (POMDP). Next, by reformulating this POMDP as a Markovian search problem, we prove that optimal missile launch control policies are threshold-based policies in nature. We then present optimal threshold policies that unlike their POMDP counterparts are computationally efficient and inexpensive to implement in real time combat systems. Numerical results demonstrate the effectiveness of these threshold based missile deployment algorithms
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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 | 0.005 |
| 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) | 0.002 | 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".