Photometric data from nonresolved objects for improved drag and reentry prediction

Debris objects such as rocket bodies can pose a threat to active space assets in orbit and to assets and humans on the ground through reentry. Orbit and reentry predictions for low-perigee resident space objects are strongly influenced by atmospheric drag. Such predictions are typically performed using fixed or fitted drag/ballistic coefficients that can result in large prediction errors. Accurate drag coefficients require, among other things, knowledge of the attitude. This paper develops an approach to compute accurate drag coefficients for debris objects toward accurate orbit and reentry predictions. The method uses a nonlinear least-squares estimator to estimate the attitude and angular velocities using light curves for debris objects with known shape models. This paper focuses on rocket bodies in particular. The estimated attitude and angular velocities are then used to compute the drag coefficients using a flat-plate panel method. The technique is validated using simulated data scenarios with a number of representative rocket body models. Good performance is observed for the developed approach. Results show that neglecting attitude variations for resident space objects in highly elliptic orbits can result in orbit errors of more than 100 km after just ten brief passes through the atmosphere.