Initial orbit determination (IOD), as a basis for initial orbit association and accurate orbit determination (OD), has a crucial role in the process of obtaining space debris orbit information. Among the traditional methods, the Gooding method has better convergence and stability. In this study, the Gooding method is enhanced to solve the issues discovered. A novel initial orbit determination (IOD) method is developed using the proposed improvement measures of the single-parameter initial value determination (SIVD) method, the fitted-curve noise suppression method, the restricted corrective value solution method, the removal of trivial solutions, etc. The experimental results verify the effectiveness of the improved method. The success rate of the initial orbit determination reached 99%, and the accuracy of the solved orbit parameters was significantly improved, especially the semi-major axis (SMA) error of less than 50 km, accounting for 88% of the total. It can be seen that the method meets the demand of space-based space debris cataloging for initial orbit association and can serve in the field of space situational awareness, which has important practical significance and application potential.

Under space-based angle-only observations, the admissible region (AR) algorithm often yields erroneous solutions in initial orbit determination (IOD) for near-circular orbit objects. This paper analyzes the mechanism generating these erroneous solutions and proposes a suppression method based on screening multi-direction solution sets using dispersion analysis. The study first points out that, under near-circular orbit constraints, the traditional AR algorithm, due to specific relative geometric configurations between the platform and the object, generates mathematically indistinguishable erroneous solutions in the range–rate space, severely affecting the reliability of solving orbital parameters such as the semi-major axis. To address this, this paper proposes a Relative Variance-based Single-Parameter Solution Screening (RV-SPSS) method. By constructing multiple observation point pairs with different geometric characteristics, the semi-major axis is solved for each pair to form a solution set. Leveraging the property that the true solution is remains consistent across different observation geometries, whereas erroneous solutions vary significantly with geometric changes, the method uses the relative variance of the solution set as a criterion to identify and eliminate erroneous solutions. Additionally, a slant range threshold is introduced to filter out trivial solutions, and polynomial fitting is applied to preprocess the observation sequence to suppress noise effects. Experimental results show that the proposed method effectively suppresses erroneous solutions. In historical data simulations based on Two-Line Element sets (TLEs), the convergence ratio to erroneous solutions was reduced from 88.8% for the original method to below 5.5%. In large-scale random orbit simulations, the erroneous solution suppression rate exceeded 94% for Low Earth Orbit (LEO), Medium Earth Orbit (MEO), and Geostationary Earth Orbit (GEO) objects. The method significantly enhances the reliability and practicality of space-based very-short-arc initial orbit determination.