Nonlinear Regression Analysis by Global Optimization: A Case Study in Space Engineering

The search for a better understanding of complex systems calls for quantitative model development. Within this development process, model fitting to observational data (calibration) often plays an important role. Traditionally, local optimization techniques have been applied to solve nonlinear (as well as linear) model calibration problems numerically: the limitations of such approaches in the nonlinear context—due to their local search scope—are well known. In order to properly address this issue, global optimization strategies can be used to find (in practice, to approximate) the best possible model parameterization. This work discusses an application of nonlinear regression model development and calibration in the context of space engineering. We study a scientific instrument, installed on-board of the International Space Station and aimed at studying the Sun’s effect on the Earth’s atmosphere. A complex sensor temperature monitoring objective has motivated the adoption of an ad hoc calibration methodology. Due to the apparent non-convexity of the underlying regression model, a global optimization approach has been implemented: the LGO software package is used to carry out the numerical optimization required periodically for each stage of the analysis. We report computational performance results and offer related insight. Our case study shows the robust and efficient performance of the global scope model calibration approach.