KEYWORDS: Black bodies, Temperature metrology, Infrared radiation, Control systems, Temperature sensors, Neural networks, Infrared imaging, Imaging systems
With the further development of the technology, the application of infrared imaging detection system environment gradually extended to the external field, high altitude, near space and outer space, etc. Its working temperature range is getting wider and wider, low temperature can reach -50°C below, high temperature can reach 70°C above. Infrared imaging detection system needs to meet the requirements of quantitative detection technology in a wide temperature range to ensure the completion of corresponding functions. In order to ensure that the infrared detection system can perform performance testing, radiometric calibration and quantitative traceability in the whole temperature range, this paper developed a large-aperture high-precision fixed-point infrared radiation source with a wide temperature range, and its phase change medium is water. It mainly includes diaphragm assembly, radiation cavity, inner wall coating of radiation cavity, heating assembly, temperature control assembly, etc. After development, verification tests were carried out in high and low temperature environment, and the following indexes were achieved: effective emissivity ≥0.999, cavity opening diameter ≥60mm, and temperature measurement uncertainty: 5mK (k=2). It has been proved that it can meet the measurement and testing requirements of infrared detection system under wide temperature range.
A method of target material recognition based on spectral emissivity curve matching is proposed to solve the problem of target material recognition in complex field environment. In this paper, the target material recognition model of spectral emissivity curve is established, and the effectiveness of the model is verified by simulation experiments. The model input data is the spectral emissivity measurement data of the target material to be measured. The model construction mainly includes weighted deviation maximization model construction, Lagrange function extremum method solution and similarity function construction. Through the simulation experiment model, the similarity between different curves is calculated and the target is effectively identified. The influence of spectral emissivity noise on similarity is analyzed. The target spectral emissivity superposition noise amplitude is 0.01 ~ 0.05 times, and the similarity is greater than 0.6. This method can accurately identify the target material. The spectral emissivity identification method proposed in this paper can provide technical support for target identification in complex
In response to the demand for remote detection of high-temperature target spectral radiation, a detection method using a large-aperture grating spectrometer is proposed and a detection system is developed. The detection system is mainly composed of a large aperture Cassegrain optical lens, a 380nm~1000nm grating spectrometer, a1000nm~2450nm grating spectrometer, the long-wave infrared imaging aiming detection components, and the spectroscopic optical components. The detection system is placed in a two-axis turntable and the long-wave infrared imaging targeting detection component is used to aim at a remote high-temperature target. After the target spectral radiation is collected by the large-aperture Cassegrain optical lens, it reaches respectively the 380nm~1000nm grating spectrometer and the 1000nm~2450nm grating spectrometer through the spectroscopic optical components. A standard blackbody of 1000℃ is used to test the performance of the detection system. The test results show that the detection accuracy of spectral radiance is within 5% at most spectral points. The remote high-temperature target spectral radiation detection method proposed in this paper can provide technical support for remote target detection, analysis, and recognition.
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