Image stabilization of airborne inertial stabilization platform using fast steering mirror
Bibliographic record
Abstract
One of the most important missions of an airborne inertial stabilization platform (AISP) is to acquire the image of the target with high resolution. It is challenging for the AISP to acquire such high-quality images because the AISP is continuously exposed to a significant level of vibration from the airplane, which is transformed into exogenous disturbance torque. As a consequence, the AISP suffers line-of-sight jitter that undermines the image quality. Although the conventional mass stabilization system is capable of rejecting exogenous disturbance torque only within a low frequency band, the introduction of a fast steering mirror (FSM) can significantly expand the disturbance rejection capacity. This paper describes how the introduction of the FSM improves the image stabilization capacity. On top of the conventional mass stabilization system of gimbaled mechanism with inertial sensor feedbacks, we implement the FSM that covers high frequency band within the optical path from the telescopes of the AISP to its image sensor. A high gain FSM controller is designed through the loop-shaping method and applied to the piezo-electric actuator driven FSM, which shows a sufficiently high bandwidth for rejecting the exogenous disturbance of our interest. The results from both actuation data and acquired images demonstrate the effectiveness of the FSM in the image stabilization of the AISP.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".