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Distributed Estimation for Formation...

Prabhu, Kaushik.

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Distributed Estimation for Formation Flying Spacecraft.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Distributed Estimation for Formation Flying Spacecraft./
作者:	Prabhu, Kaushik.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2023,
面頁冊數:	142 p.
附註:	Source: Dissertations Abstracts International, Volume: 85-05, Section: B.
Contained By:	Dissertations Abstracts International85-05B.
標題:	Aerospace engineering. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30872941
ISBN:	9798380828710

Distributed Estimation for Formation Flying Spacecraft.
Prabhu, Kaushik.

Distributed Estimation for Formation Flying Spacecraft. - Ann Arbor : ProQuest Dissertations & Theses, 2023 - 142 p.

Source: Dissertations Abstracts International, Volume: 85-05, Section: B.

Thesis (Ph.D.)--Texas A&M University, 2023.

This item must not be sold to any third party vendors.

Formation flying is a critical technology for future space missions. Distributed estimation architectures will play a key role in achieving multi-spacecraft mission objectives. In the distributed framework, each spacecraft has access only to its local measurement data and the goal is to perform collaborative estimation via information exchange with the neighboring spacecraft. This dissertation develops two types of estimation algorithms, namely, a distributed batch filter for static parameter estimation and a distributed real-time filter for dynamic state estimation. The Least Squares (LS) technique is a popular approach for batch filtering. This method finds the solution of an overdetermined system of linear equations by minimizing the sum of squares of the residuals. While the LS estimator is known to be optimal for Gaussian measurement errors, its performance degrades in the presence of gross errors (outliers) in the measurements. The Least Absolute Deviations (LAD) technique, on the other hand, finds the solution that minimizes the sum of absolute values of the residuals and is known to be robust to measurement outliers. In this dissertation, we begin by formulating a linear programming-based solution to the LAD estimation problem. The LAD solution for linear systems is implemented in a nonlinear framework to solve an orbit determination problem. Further, an estimate of the error covariance matrix for the LAD estimates is also derived. For applications in multi-agent systems, a distributed form of the LAD estimator is formulated. In the Distributed (D-) LAD algorithm, individual agents utilize local measurement data and iteratively exchange information with their immediate neighbors via single-hop communications to collaboratively compute the LAD estimate. The distributed algorithm retains the robustness properties of the central LAD estimator. The D-LAD solution for linear systems is implemented in a nonlinear framework to solve the problem of distributed orbit determination of a target body using a formation of spacecraft. For distributed real-time filtering, the problem of autonomous inertial localization of spacecraft formations is considered. In the case of large formation sizes, each spacecraft may not be able to track or communicate with all other spacecraft. Further, for formations deployed in deep space, the unavailability of the Global Navigation Satellite System makes inertial state estimation challenging. We propose the Distributed Absolute and Relative Estimation (DARE) algorithm for autonomous inertial estimation of spacecraft formations. The algorithm enables each spacecraft to maintain an accurate inertial estimate of the entire formation even in the presence of observability and communication constraints. A modified version of the algorithm called the Sparse (S-) DARE algorithm is also derived. This algorithm is computationally more efficient at the expense of estimation accuracy making it suitable for implementation on nano-satellites where resources are limited.

ISBN: 9798380828710Subjects--Topical Terms:

1002622
Aerospace engineering.
Subjects--Index Terms:

Distributed estimation

Distributed Estimation for Formation Flying Spacecraft.
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300 $a 142 p.
500 $a Source: Dissertations Abstracts International, Volume: 85-05, Section: B.
500 $a Advisor: Alfriend, Kyle T.
502 $a Thesis (Ph.D.)--Texas A&M University, 2023.
506 $a This item must not be sold to any third party vendors.
520 $a Formation flying is a critical technology for future space missions. Distributed estimation architectures will play a key role in achieving multi-spacecraft mission objectives. In the distributed framework, each spacecraft has access only to its local measurement data and the goal is to perform collaborative estimation via information exchange with the neighboring spacecraft. This dissertation develops two types of estimation algorithms, namely, a distributed batch filter for static parameter estimation and a distributed real-time filter for dynamic state estimation. The Least Squares (LS) technique is a popular approach for batch filtering. This method finds the solution of an overdetermined system of linear equations by minimizing the sum of squares of the residuals. While the LS estimator is known to be optimal for Gaussian measurement errors, its performance degrades in the presence of gross errors (outliers) in the measurements. The Least Absolute Deviations (LAD) technique, on the other hand, finds the solution that minimizes the sum of absolute values of the residuals and is known to be robust to measurement outliers. In this dissertation, we begin by formulating a linear programming-based solution to the LAD estimation problem. The LAD solution for linear systems is implemented in a nonlinear framework to solve an orbit determination problem. Further, an estimate of the error covariance matrix for the LAD estimates is also derived. For applications in multi-agent systems, a distributed form of the LAD estimator is formulated. In the Distributed (D-) LAD algorithm, individual agents utilize local measurement data and iteratively exchange information with their immediate neighbors via single-hop communications to collaboratively compute the LAD estimate. The distributed algorithm retains the robustness properties of the central LAD estimator. The D-LAD solution for linear systems is implemented in a nonlinear framework to solve the problem of distributed orbit determination of a target body using a formation of spacecraft. For distributed real-time filtering, the problem of autonomous inertial localization of spacecraft formations is considered. In the case of large formation sizes, each spacecraft may not be able to track or communicate with all other spacecraft. Further, for formations deployed in deep space, the unavailability of the Global Navigation Satellite System makes inertial state estimation challenging. We propose the Distributed Absolute and Relative Estimation (DARE) algorithm for autonomous inertial estimation of spacecraft formations. The algorithm enables each spacecraft to maintain an accurate inertial estimate of the entire formation even in the presence of observability and communication constraints. A modified version of the algorithm called the Sparse (S-) DARE algorithm is also derived. This algorithm is computationally more efficient at the expense of estimation accuracy making it suitable for implementation on nano-satellites where resources are limited.
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650 4 $a Aerospace engineering. $3 1002622
650 4 $a Astrophysics. $3 535904
650 4 $a Mechanical engineering. $3 649730
653 $a Distributed estimation
653 $a Spacecraft formation flying
653 $a Least Absolute Deviations
653 $a Gross errors
690 $a 0538
690 $a 0596
690 $a 0548
710 2 $a Texas A&M University. $b Aerospace Engineering. $3 3169098
773 0 $t Dissertations Abstracts International $g 85-05B.
790 $a 0803
791 $a Ph.D.
792 $a 2023
793 $a English
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30872941