Doctoral Dissertations


“The continued development of small satellites (SmallSats) has made them an increasingly viable mission alternative to traditional monolithic spacecraft. Constellations, swarms, and formations of these small spacecraft have the potential to fill unique gaps in the space systems architecture, while reducing overall mission costs and increasing mission redundancy. Cooperative navigation between spacecraft within swarms and formations is critical to mission success, but poses many challenges for SmallSats due to their size, mass, power, and computing constraints. While Earth orbiting missions can rely on GNSS data for high-accuracy inertial and relative navigation, deep space missions require new navigation techniques. In this work, the swarm/formation navigation problem is divided into two parts: spacecraft identification/data association and relative pose estimation. This research presents a solution to the spacecraft identification and data association problem by using an unsupervised learning (clustering) architecture that classifies spacecraft from monocular camera images. The algorithm presented detects objects in the field of view of a camera on an "observer" member of the swarm and continually tracks them over time by assigning incoming data to the correct spacecraft cluster. Monte Carlo analysis results show high levels of classification precision, accuracy, and recall over a range of swarm parameters of interest. High fidelity simulations performed using Analytical Graphics Inc. STK software with a swarm of spacecraft deployed in a lunar orbit demonstrate the ability of the algorithm to adapt as the swarm configuration changes throughout the simulation. They also reveal the algorithm’s robustness to missing measurements during adverse lighting conditions. Incremental cluster validity indices were also used to quantify the performance of the clustering algorithm, and the transient trends of these indices have been shown to provide insights into the swarm behaviors. The developed approach shows good potential for providing an effective means by which the spacecraft identification and data association process can be realized autonomously in near-real time by small satellites”--Abstract, page iii.


Pernicka, Henry J.

Committee Member(s)

Wunsch, Donald C.
Sarangapani, Jagannathan, 1965-
Han, Daoru Frank
Schmidt, Jillian B.


Mechanical and Aerospace Engineering

Degree Name

Ph. D. in Aerospace Engineering


Missouri University of Science and Technology

Publication Date

Fall 2021


xi, 131 pages

Note about bibliography

Includes bibliographic references (pages 126-130).


© 2021 Jill Christine Davis, All rights reserved.

Document Type

Dissertation - Open Access

File Type




Thesis Number

T 11943