When the Satellite-borne very high infrared spectrometer performs atmospheric composition analysis, it needs to use the sun as a light source for occultation detection. The stability of the light source is related to the detection accuracy. In general, it is believed that the light distribution within the maximum solar intensity range is the most stable and it is necessary to accurately identify and track this range. However, due to the inhomogeneous atmosphere and the change of atmospheric density, the solar image will have deformation or block, which affects the accuracy of target recognition and tracking. This paper proposes a multi-objective clustering-based recognition method to solve the problem of accurate multi-target identification under the large dynamic light intensity range, and uses the hardware structure of FPGA+DSP to realize the imaging and tracking system.
When sun is used as the light source for atmospheric composition detection, it is necessary to image sun for accurate identification and stable tracking. In the course of 180 second of the occultation, the magnitude of sun light intensity through the atmosphere changes greatly. It is nearly 1100 times illumination change between the maximum atmospheric and the minimum atmospheric. And the process of light change is so severe that 2.9 times per second of light change can be reached. Therefore, it is difficult to control the integration time of sun image camera. In this paper, a novel adaptive integration time control method for occultation is presented. In this method, with the distribution of gray value in the image as the reference variable, and the concepts of speed integral PID control, the integration time adaptive control problem of high frequency imaging. The large dynamic range integration time automatic control in the occultation can be achieved.