Observability and Estimation of Distributed Space Systems via Local Information-exchange Networks
Author(s) -
Amirreza Rahmani,
Mehran Mesbahi,
Nanaz Fathpour,
Fred Y. Hadaegh
Publication year - 2008
Publication title -
aiaa guidance, navigation, and control conference and exhibit
Language(s) - English
Resource type - Conference proceedings
DOI - 10.2514/6.2008-7010
Subject(s) - observability , estimation , computer science , information exchange , space (punctuation) , distributed computing , mathematics , telecommunications , engineering , systems engineering , operating system
In this work, we develop an approach to formation estimation by explicitly characterizing formation’s system-theoretic attributes in terms of the underlying inter-spacecraft information-exchange network. In particular, we approach the formation observer and estimator design by relaxing the accessibility to the global state information by a centralized observer or estimator- and in turn- providing an analysis and synthesis framework for formation observers and estimators that rely on local measurements. The novelty of our approach hinges upon the explicit examination of the underlying distributed spacecraft network in the realm of guidance, navigation, and control algorithmic analysis and design. The overarching goal of our general research program, some of whose results are reported in this paper, is the development of distributed spacecraft estimation algorithms that are scalable, modular, and robust to variations in the topology and link characteristics of the formation information exchange network. In this work, we consider the observability of a spacecraft formation from a single observation node and utilize the agreement protocol as a mechanism for observing formation states from local measurements. Specifically, we show how the symmetry structure of the network, characterized in terms of its automorphism group, directly relates to the observability of the corresponding multi-agent system. The ramification of this notion of observability over networks is then explored in the context of distributed formation estimation.
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