<title>A fuzzy-logic-based tracker for a homing guided missile</title>
Bibliographic record
Abstract
The research conducted in the last decade in missile design has mainly focused in the area of guidance and control. Many researchers have designed interceptors with high performance; namely: ranging from the classical control to the knowledge based techniques. The homing guided missile flight simulation testbed has been developed and tested against different control systems. The missile aerodynamic model has been simulated based on NASA reports and the output aerodynamic coefficients have been compared and justified by the wind tunnel tests as well. The other missile modules have been simulated and compared to the real missile modules in terms of input/output experimental results. The guidance and control system has yield excellent performance against incoming and outgoing maneuvering targets falling within the missile's destruction zone. However, all the test scenarios assumed that the target information from the missile seeker (tracker) is exact and obtained from the observations without any major difficulty. In the case of high density clutter and false alarms as well as the low signal-to-noise-ratio (SNR) which may be due to the existence of flair, decoy or any other counter measure, the tracker accuracy plays an important role in the over all engagement scenario. In this paper, a fuzzy logic-based technique has been employed to improve the performance of the missile seeker at high density clutter and low SNR. The Interacting Multiple Model Fuzzy Data Association (IMM-FDA) has been employed to improve the missile-target intercept accuracy.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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".