Selection of optimal initial orbit of Near-Earth Asteroids based on Multi-Criteria integration

Abstract Initial Orbit determination (IOD) is a critical step in cataloging Near-Earth Asteroids (NEAs). Traditional IOD methods, such as the Laplace, Gauss, and double-r iteration methods, typically generate multiple solutions. However, there has been limited research on selecting the optimal solution from these multiple initial orbit solutions. This paper proposes a multi-criteria integration method for selection of optimal initial orbit solution. In addition to using observational angular residuals, we introduce three new criteria: the variance of absolute magnitude, angular velocity deviation, and the joint probability density of orbit elements. An optimization algorithm is designed to determine the weight coefficients for each criterion. Both training and testing datasets were generated using real NEA data from the Minor Planet Center database. Numerical simulations demonstrate that the proposed method effectively identifies the optimal solution from a set of multiple initial orbit solutions, achieving a correct rate of 96.52% on the training set and 96.00% on the test set.