IRCM spectral signature measurements instrumentation featuring enhanced radiometric accuracy
Why this work is in the frame
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Bibliographic record
Abstract
Hyperspectral Infrared (IR) signature measurements are performed in military applications including aircraft- and –naval vessel stealth characterization, detection/lock-on ranges, and flares efficiency characterization. Numerous military applications require high precision measurement of infrared signature characterization. For instance, Infrared Countermeasure (IRCM) systems and Infrared Counter-Countermeasure (IRCCM) system are continuously evolving. Infrared flares defeated IR guided seekers, IR flares became defeated by intelligent IR guided seekers and Jammers defeated the intelligent IR guided seekers [7]. A precise knowledge of the target infrared signature phenomenology is crucial for the development and improvement of countermeasure and counter-countermeasure systems and so precise quantification of the infrared energy emitted from the targets requires accurate spectral signature measurements. Errors in infrared characterization measurements can lead to weakness in the safety of the countermeasure system and errors in the determination of detection/lock-on range of an aircraft. The infrared signatures are analyzed, modeled, and simulated to provide a good understanding of the signature phenomenology to improve the IRCM and IRCCM technologies efficiency [7,8,9]. There is a growing need for infrared spectral signature measurement technology in order to further improve and validate infrared-based models and simulations. The addition of imagery to Spectroradiometers is improving the measurement capability of complex targets and scenes because all elements in the scene can now be measured simultaneously. However, the limited dynamic range of the Focal Plane Array (FPA) sensors used in these instruments confines the ranges of measurable radiance intensities. This ultimately affects the radiometric accuracy of these complex signatures. We will describe and demonstrate how the ABB hyperspectral imaging spectroradiometer features enhanced the radiometric accuracy of spectral signature measurements of infrared military targets.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it