Attitude Mode Estimation from Computer Vision Data via Multiple Model Methods
Abstract
One of the foundational needs for space situational awareness (SSA) is ability to assess patterns of life for resident space objects (RS0s) in order to establish a baseline for normalcy, and to then be able to detect changes from those patterns with the ultimate goal of providing indications and warning as well as assessments of intent. The attitude control system control mode (e.g. Sun-pointing, Earth-pointing, detumbling, etc.) is one element of spacecraft behavior that can be used to assess intent, which is an important aspect lo improve SSA. The main objective of the proposed work is to develop algorithms that can estimate the attitude mode of an unknown RSO. A novel approach to meet this objective is proposed based on a class of multiple-model adaptive estimation (MMAE) algorithms. These algorithms use a bank of filters to provide multiple RSO state estimates, where each filter is purposefully dependent on a mutuall unique RSO model. Each model in the MMAE is equivalent to a specific attitude mode. Estimates on the conditional probability of each model (mode) given the available measurements are provided from the MMAE approach. Here it is assumed attitude measurements, in the form of quaternions, are provided by computer vision data. The anticipated outcome of the proposed research is a unique set of novel algorithms that can be used as part of a more encompassing tool-set to gain needed knowledge of an unknown RSO s behavior. The proposed research also has a significant public purpose in that it can be used to determine the attitude motion of debris objects, which can then be used to estimate their attributes in order to determine more detailed models of them, and thus better predict their orbital behavior in order to avoid any possible collisions with active spacecraft.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Aug 01, 2019
- Source ID
- FA94531810008
Entities
People
- John Crassidis
Organizations
- Air Force Research Laboratory
- Research Foundation for the State University of New York
- United States Air Force