IMAN is a Python tool that provides inertial sensor-based estimates of spacecraft trajectories within an atmospheric influence. It provides Kalman filterderived spacecraft state estimates based upon data collected onboard, and is shown to perform at a level comparable to the conventional methods of spacecraft navigation in terms of accuracy and at a higher level with regard to the availability of results immediately after completion of an atmospheric drag pass. A benefit of this architecture is that this technology is conducive to onboard data processing and estimation and thus can compute near realtime spacecraft state estimates making it suitable for autonomous operations and/or closed-loop guidance, navigation, and control strategies.

This tool can be used to reliably predict subsequent periapsis times and locations over all aerobraking regimes. It also yields accurate peak dynamic pressure and heating rates, which are critical for a successful corridor control strategy. These data are comparable to radiometric-based navigation team reconstructed values. IMAN also provides the first instance of the use of the Unscented Kalman Filter (UKF) for the purpose of estimating an actual spacecraft trajectory arc about another planet. A significant advantage to the implementation of this type of filter is that the UKF is a non-linear filter and thus accurate to at least second order. It provides more meaningful and realistic covariances and has been shown to be robust in the presence of sparse data sets.

Currently, IMAN is being used in an experiment to demonstrate Inertial Measurement Unit (IMU)-based aerobraking navigation for the Mars Reconnaissance Orbiter (MRO). It also can be used in other operational missions such as those using the atmosphere for entry-descent-landing or solar sail missions that experience significant solar radiation pressure for propulsion.

This program was written by Moriba Jah, Michael Lisano, and George Hockney of Caltech for NASA's Jet Propulsion Laboratory.

This software is available for commercial licensing. Please contact Karina Edmonds of the California Institute of Technology at (626) 395-2322. Refer to NPO-43677.