Design of optimal observation strategy for re-entry prediction improvement of GTOs upper stage

Abstract

In this paper, an automatic approach to design the observation strategy of spent upper stage moving on GTOs is presented. More specifically, the design is formulated as a multi-objective optimization problem solved by means of a multi-objective genetic algorithm (MOGA). This approach allows minimizing both the number of total measurements required to detect the object and the error on re-entry prediction. Within the optimization process a nonlinear OD algorithm is run to determine the estimates of both initial state and model parameters and the associated covariance matrix. The Nonlinear Least Square Estimator (NLSE) technique is implemented, exploiting the differential algebra framework for Jacobian matrix computation in order to reduce the computational effort related to OD problem solution. Finally, the software tool IRIS is developed to accurately simulate the observation campaign based on geometry and constraints of existing sensors currently available to the European Space Agency (ESA). Numerical simulations are performed to demonstrate the efficiency of the proposed approach.

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