Engineering a Low-Cost Remote Sensing Capability for Deep-Space Applications

Abstract

Systems engineering (SE) has been a useful tool for providing objective processes to breaking down complex technical problems to simpler tasks, while concurrently generating metrics to provide assurance that the solution is fit-for-purpose. Tailored forms of SE have also been used by cubesat mission designers to assist in reducing risk by providing iterative feedback and key artifacts to provide managers with the evidence to adjust resources and tasking for success. Cubesat-sized spacecraft are being planned, built and in some cases, flown to provide a lower-cost entry point for deep-space exploration. This is particularly important for agencies and countries with lower space exploration budgets, where specific mission objectives can be used to develop tailored payloads within tighter constraints, while also returning useful scientific results or engineering data. In this work, a tailored SE tradespace approach was used to help determine how a 6 unit (6U) cubesat could be built from commercial-off-the-shelf (COTS)-based components and undertake remote sensing missions near Mars or near-Earth Asteroids. The primary purpose of these missions is to carry a hyperspectral sensor sensitive to 600-800nm wavelengths (hereafter defined as “red-edge”), that will investigate mineralogy characteristics commonly associated with oxidizing and hydrating environments in red-edge. Minerals of this type remain of high interest for indicators of present or past habitability for life, or active geologic processes. Implications of operating in a deep-space environment were considered as part of engineering constraints of the design, including potential reduction of available solar energy, changes in thermal environment and background radiation, and vastly increased communications distances. The engineering tradespace analysis identified realistic COTS options that could satisfy mission objectives for the 6U cubesat bus while also accommodating a reasonable degree of risk. The exception was the communication subsystem, in which case suitable capability was restricted to one particular option. This analysis was used to support an additional trade investigation into the type of sensors that would be most suitable for building the red-edge hyperspectral payload. This was in part constrained by ensuring not only that readily available COTS sensors were used, but that affordability, particularly during a geopolitical environment that was affecting component supply surety and access to manufacturing facilities, was optimized. It was found that a number of sensor options were available for designing a useful instrument, although the rapid development and life-of-type issues with COTS sensors restricted the ability to obtain useful metrics on their performance in the space environment. Additional engineering testing was conducted by constructing hyperspectral sensors using sensors popular in science, technology, engineering and mathematics (STEM) contexts. Engineering and performance metrics of the payload containing the sensors was conducted; and performance of these sensors in relevant analogous environments. A selection of materials exhibiting spectral phenomenology in the red-edge portion of the spectrum was used to produce metrics on the performance of the sensors. It was found that low-cost cameras were able to distinguish between most minerals, although they required a wider spectral range to do so. Additionally, while Raspberry Pi cameras have been popular with scientific applications, a low-cost camera without a Bayer filter markedly improved spectral sensitivity. Consideration for space-environment testing was also trialed in additional experiments using high-altitude balloons to reach the near-space environment. The sensor payloads experienced conditions approximating the surface of Mars, and results were compared with Landsat 7, a heritage Earth sensing satellite, using a popular vegetation index. The selected Raspberry Pi cameras were able to provide useful results from near-space that could be compared with space imagery. Further testing incorporated comparative analysis of custom-built sensors using readily available Raspberry Pi and astronomy cameras, and results from Mastcam and Mastcam/z instruments currently on the surface of Mars. Two sensor designs were trialed in field settings possessing Mars-analogue materials, and a subset of these materials were analysed using a laboratory-grade spectro-radiometer. Results showed the Raspberry Pi multispectral camera would be best suited for broad-scale indications of mineralogy that could be targeted by the pushbroom sensor. This sensor was found to possess a narrower spectral range than the Mastcam and Mastcam/z but was sensitive to a greater number of bands within this range. The pushbroom sensor returned data on spectral phenomenology associated with attributes of Minerals of the type found on Mars. The actual performance of the payload in appropriate conditions was important to provide critical information used to risk reduce future designs. Additionally, the successful outcomes of the trials reduced risk for their application in a deep space environment. The SE and practical performance testing conducted in this thesis could be developed further to design, build and fly a hyperspectral sensor, sensitive to red-edge wavelengths, on a deep-space cubesat mission. Such a mission could be flown at reasonable cost yet return useful scientific and engineering data