Session
Pre-Conference Workshop Session VIII: Communications
Location
Utah State University, Logan, UT
Abstract
The small satellite Flying Laptop, launched in July 2017, was developed and built by graduate and undergraduate students at the Institute of Space Systems of the University of Stuttgart with support by space industry and research institutions. The mission goals are technology demonstration, earth observation, and serving as an educational satellite. At a mass of 110 kg, it features three-axis stabilized attitude control and several payloads, including an AIS receiver, a multi spectral camera system, a wide angle camera, and an optical communication terminal.
The pointing requirement for the optical communication is an accuracy of less than 150 arcseconds during a target overflight. To fulfill this requirement, several measures are needed. A major part of them is the characterization of the attitude control system (ACS). Since there is no optical receiver onboard, it is not possible to perform closed loop tracking of the satellite attitude. Therefore, the absolute performance and the characteristic noise levels of the attitude control system, can only be determined with other payloads. In this case the multi-spectral camera system was used, providing a ground resolution of 25 m. To use the images from the satellite to improve the ACS, three steps have to be taken. As a first action, the images have to be georeferenced to know the position of each pixel in the WGS84 coordinate system. With this information, the deviation of the image center from the desired target is measured. This second step includes the calculation of the deviation matrix. To avoid a corruption of the attitude control of the satellite, the matrix is checked for unrealistic values in a third and final step. These three actions can be repeated as needed without human interaction.
By updating the ACS model onboard the satellite, the results of the image processing are used to correct the off-pointing. This deviation is time invariant and is caused by an insufficient alignment of the satellite axes and the cameras on ground. In contrast to that, characterizing noise as a time variant factor, the ACS is tested over a long period of time. This is achieved by analyzing images from one, as well as from multiple target overflights. This conquers the issue of a very low image rate while observing high frequency attitude changes. Using this mechanism, the proposed process can be used to continuously monitor the pointing quality.
As a first approach the described processing is done manually by comparing the target position on Earth with the center of the taken image. The method successfully showed an improvement of the pointing in the pictures, paving the way for their automation. This paper gives an overview of the needed image processing and tools to automatically use cameras on board the satellite to validate and improve the ACS periodically. First results of the long term characteristics and pointing improvements are shown.
Pointing Enhancement for an Optical Laser Downlink Using Automated Image Processing
Utah State University, Logan, UT
The small satellite Flying Laptop, launched in July 2017, was developed and built by graduate and undergraduate students at the Institute of Space Systems of the University of Stuttgart with support by space industry and research institutions. The mission goals are technology demonstration, earth observation, and serving as an educational satellite. At a mass of 110 kg, it features three-axis stabilized attitude control and several payloads, including an AIS receiver, a multi spectral camera system, a wide angle camera, and an optical communication terminal.
The pointing requirement for the optical communication is an accuracy of less than 150 arcseconds during a target overflight. To fulfill this requirement, several measures are needed. A major part of them is the characterization of the attitude control system (ACS). Since there is no optical receiver onboard, it is not possible to perform closed loop tracking of the satellite attitude. Therefore, the absolute performance and the characteristic noise levels of the attitude control system, can only be determined with other payloads. In this case the multi-spectral camera system was used, providing a ground resolution of 25 m. To use the images from the satellite to improve the ACS, three steps have to be taken. As a first action, the images have to be georeferenced to know the position of each pixel in the WGS84 coordinate system. With this information, the deviation of the image center from the desired target is measured. This second step includes the calculation of the deviation matrix. To avoid a corruption of the attitude control of the satellite, the matrix is checked for unrealistic values in a third and final step. These three actions can be repeated as needed without human interaction.
By updating the ACS model onboard the satellite, the results of the image processing are used to correct the off-pointing. This deviation is time invariant and is caused by an insufficient alignment of the satellite axes and the cameras on ground. In contrast to that, characterizing noise as a time variant factor, the ACS is tested over a long period of time. This is achieved by analyzing images from one, as well as from multiple target overflights. This conquers the issue of a very low image rate while observing high frequency attitude changes. Using this mechanism, the proposed process can be used to continuously monitor the pointing quality.
As a first approach the described processing is done manually by comparing the target position on Earth with the center of the taken image. The method successfully showed an improvement of the pointing in the pictures, paving the way for their automation. This paper gives an overview of the needed image processing and tools to automatically use cameras on board the satellite to validate and improve the ACS periodically. First results of the long term characteristics and pointing improvements are shown.
