Abstract
Distributed Space Missions (DSMs) are gaining momentum in their application to earth science missions owing to their unique ability to increase observation sampling in spatial, spectral, angular and temporal dimensions simultaneously. Since DSM architectures are defined by monolithic architecture variables and variables associated with the distributed framework, they have many and often conflicting design variables and objectives. There are very few open-access tools available to explore the tradespace of variables, minimize cost and maximize performance for pre-defined science goals, and therefore select the most optimal constellation design. This paper presents a software tool, developed on the MATLAB engine interfaced with STK, that is based on tightly coupled science and engineering models. It can generate hundreds of DSM architectures based on pre-defined design variable ranges and size those architectures in terms of pre-defined science and cost metrics. The tool’s performance analysis module is driven by the concept of observing system simulation experiments (OSSE), traditionally used to validate proposed instruments. The architecture and simulated measurement generation is driven by Model-Based Systems Engineering (MBSE). The utility of the tool is demonstrated using a case study to determine the Earth’s global, diurnal Radiation budget more accurately than current monolithic instruments.
Satellite Constellation Mission Design using Model-Based Systems Engineering and Observing System Simulation Experiments
Distributed Space Missions (DSMs) are gaining momentum in their application to earth science missions owing to their unique ability to increase observation sampling in spatial, spectral, angular and temporal dimensions simultaneously. Since DSM architectures are defined by monolithic architecture variables and variables associated with the distributed framework, they have many and often conflicting design variables and objectives. There are very few open-access tools available to explore the tradespace of variables, minimize cost and maximize performance for pre-defined science goals, and therefore select the most optimal constellation design. This paper presents a software tool, developed on the MATLAB engine interfaced with STK, that is based on tightly coupled science and engineering models. It can generate hundreds of DSM architectures based on pre-defined design variable ranges and size those architectures in terms of pre-defined science and cost metrics. The tool’s performance analysis module is driven by the concept of observing system simulation experiments (OSSE), traditionally used to validate proposed instruments. The architecture and simulated measurement generation is driven by Model-Based Systems Engineering (MBSE). The utility of the tool is demonstrated using a case study to determine the Earth’s global, diurnal Radiation budget more accurately than current monolithic instruments.
