Statistical Risk Estimation for Communication System Design: Development of Optimization Frameworks

摘要

~1The design of a spacecraft is an evolutionary process that starts from requirements and evolves over time across different design phases. During this process, a lot of changes can happen. They can affect mass and power at component level, at subsystem level, and even at system level. Each spacecraft has to meet overall constraints in terms of mass and power: for this reason, it's important to be sure that the design does not exceed these limitations. Current practice in system modeling deals with this problem by allocating margins on single components and on each of the subsystems. However, a statistical characterization of these fluctuations in mass and power is missing, and the consequence is a design that either is too risky and does not fit the mission constraints, or is too conservative and generates an inefficient utilization of resources. Hence, the objective of this research is to develop a mathematical approach to quantify the likelihood that the design would meet the spacecraft and mission constraints while the design matures. Due to the complexity of the problem and to the different expertise and knowledge required to develop a complete risk model for all the different subsystems, the research is focused on risk estimation for a specific subsystem: communication. Communication constitutes a key design driver in many different spacecraft, and it is also the core in the design of commercial satellite applications. Moreover, the current research aims to be a "proof of concept," which can then be further expanded to the different subsystems, as well as to the whole spacecraft design process. Particularly important in this analysis is the development of optimization frameworks to compare different design architectures, and to select the one that achieves design objectives, like minimal mass and power consumption, while minimizing the risk associated with these same metrics. The article is structured as follows: an overview of the model to perform statistical risk estimation is described, then the mathematical framework for optimization is detailed and applied, and finally results are presented.