Relative navigation of uncooperative space bodies

Scott P. Geyer, John L. Crassidis, Manoranjan Majji, Roshan Thomas Eapen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper develops a novel approach for relative navigation of two space bodies for cases in which communication with the target space body is either inconvenient or impossible. The approach utilizes multiple hypothesis tracking (MHT) to track the features detected from optical image data using standard edge detection techniques. This is in contrast to traditional optical-only relative navigation systems that use other approaches such as machine learning techniques or optical flow techniques to track features. In machine learning, the algorithms must first be trained through an extensive set of testing scenarios to learn an end-to-end solution, and must be retrained for every particular target model-frame. Optical flow algorithms also have their own inherent limitations, such as dealing with discontinuities of motion across object boundaries in the scene. MHT uses state estimate information within its decision logic, allowing it to make data association decisions that are more robust to relative navigation scenarios than the traditional methods. The tracked features from MHT are utilized as measurements in a multiplicative extended Kalman Filter (MEKF). Using the MEKF to feed predicted states into the MHT algorithm, which in turn provides measurements back to the MEKF, estimates of the attitude, angular rate, position and velocity of the target body are obtained.

Original languageEnglish (US)
Title of host publicationAIAA Scitech 2021 Forum
PublisherAmerican Institute of Aeronautics and Astronautics Inc, AIAA
Pages1-17
Number of pages17
ISBN (Print)9781624106095
StatePublished - 2021
EventAIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2021 - Virtual, Online
Duration: Jan 11 2021Jan 15 2021

Publication series

NameAIAA Scitech 2021 Forum

Conference

ConferenceAIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2021
CityVirtual, Online
Period1/11/211/15/21

All Science Journal Classification (ASJC) codes

  • Aerospace Engineering

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