A PSD-based solar spot position detection system is developed for solar tracking closed-loop control of mobile SOF-FTIR (Solar Occultation Flux method based on Fourier Transform Infrared spectrometer). The positioning error factors of PSD (position sensitive detector) are analyzed in detail. A voltage model for PSD signal conditioning circuit has been established to investigate the noise factors. The model shows that the positioning error is mainly related to PSD dark current and circuit gain. A static voltage deduction calibration method based on genetic algorithm is proposed to eliminate the effect of dark current. The gain ratio between channels is calculated based on the fitting curve slope of discrete position data of PSD center point with different light intensity for circuit gain calibration. The positioning accuracy and precision are greatly enhanced, especially when the light intensity is weak, compared with uncalibrated results. The positioning accuracy of center, middle and edge areas of PSD can reach 0.14%, 0.49%, and 1.09%, respectively, after correction in the range of light intensity voltage from 40 mV to 20 V. The corresponding standard deviations of each region are 0.005, 0.009, and 0.014, respectively. The adjustment methods proposed in this paper improve both measurement accuracy and detection limit. The results demonstrate that the calibrated PSD positioning accuracy can meet the requirements of SOF-FTIR for solar tracking.
The concentration-path-length product (CL) image of the leaking gas cloud measured by the passive Fourier transform infrared (FTIR) scanning remote-sensing imaging system has a low resolution. Gas cloud diffusion is affected by wind speed and direction, which makes it difficult to trace the source of a leakage. Therefore, we propose a method to reconstruct the CL image of the leaking gas cloud applied to the passive FTIR scanning remote-sensing imaging system. First, bicubic interpolation is employed to upsample the low-resolution CL image of gas clouds. Second, the maximum noise-equivalent concentration-path-length (NECL) product is used as a threshold to segment the high-resolution gas cloud image. Third, image morphology processing and the evaluation criteria of the leaking gas cloud are applied to detect the leaking gas cloud. Finally, the high-resolution CL image of the leaking gas cloud is superimposed onto the background image. The effectiveness of the reconstruction method is proven by the S F 6 remote-sensing experiment and simulation. The results show that the proposed method should be effectively implemented to reconstruct the high-resolution CL image of the leaking gas cloud. The reconstructed leaking gas cloud plume, as well as the location of the leakage source, are quite obvious. The reconstruction method has been successfully applied to passive FTIR scanning remote-sensing imaging systems, with high accuracy, in real time, and with robustness.
The sky infrared background radiation varies greatly with spatial distribution and time. When Scanning Fourier Transform Infrared (FTIR) remote sensing imaging system scans the target gas cloud with the sky as the background, the background radiation corresponding to each scanned pixel is different, and the background does not have a constant baseline. It is extremely difficult to obtain the background spectrum of each pixel in real-time, which affects the inversion accuracy of the target gas cloud transmittance. An inversion method of target gas cloud transmittance based on atmospheric profile synthesis background is proposed. The temperature, humidity, pressure, and ozone profiles of the measured locations and the atmospheric model are used to generate the sky infrared background in order to solve the problem that it is difficult to measure the clean sky infrared background spectrum in the chemical industry park. This paper proposes that there is a continuous derivable relationship between the sky infrared background spectrum and the cosine of zenith angle at each wavenumber, so a small amount of sky infrared background spectrum with a zenith angle gradient can quickly generate sky infrared background spectrum at any elevation angle. The proposed method is verified by the Moderate Resolution Atmospheric Radiative Transfer Model (MODTRAN) software simulation and the remote sensing imaging experiment of SF<sub>6</sub> gas. The proposed method can quickly generate the sky infrared background spectrum corresponding to any angle within the gradient elevation angle and accurately invert the target gas cloud transmittance at each pixel. The results show that the distribution trend of the column concentration of the SF<sub>6</sub> gas cloud is consistent with the actual distribution, the correlation is 0.99979.
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