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Michael A Shoemaker

Publications and source records attributed to Michael A Shoemaker.

Terrain Relative Navigation in a Lunar Landing Scenario Using autoNGC

NASA Goddard Space Flight Center is developing Autonomous Navigation Guidance and Control (autoNGC) as a flight software system for future onboard use for missions in a variety of orbital regimes, including cislunar space and beyond. This paper describes processor-in-the-loop (PIL) testing using a lunar landing scenario with terrain relative navigation (TRN) and weak-signal GPS. We give an overview of the autoNGC project and describe preliminary navigation simulation results. We also describe the TRN PIL tests on a flight-like development board, using simulated images rendered from Lunar Reconnaissance Orbit high-resolution digital terrain models. The navigation simulations show that weak-signal GPS combined with TRN during a descent from a low lunar parking orbit results in sufficiently low navigation uncertainties to support such a mission profile independent of ground-based navigation. The PIL tests show that onboard image processing and landmark correlation is achievable at a sufficiently high measurement rate.

Michael A Shoemaker↗

New Optical Navigation Results Using Historical MESSENGER Data

This paper describes new optical navigation (OpNav) results obtained by processing previously collected measurements from the MESSENGER mission to Mercury. This project also serves to mature the tools and capabilities of NASA Goddard Space Flight Center (GSFC) in OpNav, using the open source Goddard Image Analysis and Navigation Tool (GIANT). New navigation measurements are obtained during the Mercury flyby and orbital phases, using OpNav measurements generated by GIANT, and these measurements are compared to predictions. The results obtained provide a set of improvements to be made in navigation tools and will pave the way for future missions to navigate near terrestrial bodies using optical measurements.

Optical Navigation↗

A Numerical Method for Computing the State Transition Matrix Using Poincare Integral Invariants

The Poincare integral invariants describe the volumes of sets in Hamiltonian phase space. We use these invariants to derive a new numerical procedure for obtaining the state transition matrix (STM), which can be applied to both conservative and nonconservative systems. The method is analogous to a finite difference approximation of the STM, where perturbed states are numerically propagated along with the reference trajectory. We discuss the mathematical similarities between this new STM and existing methods, show numerical results for orbital motion and uncertainty propagation, and discuss new insights afforded by the Hamiltonian properties of phase flow.

state transition matrix↗