Autonomous lost in space and time state determination using periodic variable stars

Hou, Linyi

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https://hdl.handle.net/2142/129423 Copy

Description

Title

Autonomous lost in space and time state determination using periodic variable stars

Author(s)

Hou, Linyi

Issue Date

2025-04-23

Director of Research (if dissertation) or Advisor (if thesis)

• Eggl, Siegfried
• Putnam, Zachary R

Doctoral Committee Chair(s)

Eggl, Siegfried

Committee Member(s)

• Bretl, Timothy
• Ricker, Paul M

Department of Study

Aerospace Engineering

Discipline

Aerospace Engineering

Degree Granting Institution

University of Illinois Urbana-Champaign

Degree Name

Ph.D.

Degree Level

Dissertation

Keyword(s)

• Spacecraft Navigation
• Autonomous Navigation
• Variable Star
• Lost in Space
• XNAV

Abstract

The lost-in-space-and-time problem refers to the challenge of performing onboard state determination by a spacecraft with limited prior knowledge and no external communication. The problem is motivated by the fact that onboard knowledge of spacecraft position, velocity, attitude, and time can become corrupted or severely outdated due to power or data loss. Under these circumstances, spacecraft navigation methods that rely on communication with external sources such as ground stations on Earth may not be feasible since the spacecraft orientation needed to successfully achieve external communication is unknown. Solutions to the lost-in-space-and-time problem primarily use observations of celestial objects, particularly ones in the Solar System, for navigation and attitude determination. Most lost-in-space-and-time algorithms leverage the high sensitivity of Solar System objects' relative position with respect to the observer to perform navigation. However, the same sensitivity also makes it difficult to find and identify these celestial objects without some prior knowledge of the spacecraft state. Furthermore, dependence on illumination by the Sun largely constrains these methods to the inner Solar System. In contrast to these techniques, X-ray pulsar navigation uses specialized detectors that measure the timing of X-ray photons emitted by distant rotating neutron stars to determine position. X-ray pulsar navigation is potentially feasible anywhere in the Solar System or beyond, but existing literature has not explored the utility of other variable stellar sources or different ways in which they can be used. This thesis develops techniques for autonomous, lost-in-space-and-time navigation and attitude determination using variable stars. To address the ambiguity in periodic variable star signals, a search algorithm is developed which accounts for nonlinear perturbations to signal time of arrival in order to improve solution accuracy. It is shown that parallax and time dilation can significantly affect X-ray pulsar navigation accuracy and success rate. The computational feasibility of performing X-ray pulsar navigation with the proposed algorithm is demonstrated. Prior knowledge requirements on position and time are quantified. The X-ray pulsar navigation concept is extended to the visible regime with ẟ Scuti variable stars. An algorithm is developed to recover both position and time from ẟ Scuti star light curves. Navigation simulations of NASA's OSIRIS-APEX mission indicate that position and timing accuracies under 0.03 au (3σ) and 3s (3σ) can be readily achieved with existing spacecraft cameras. This coarse position and time estimate is shown to be sufficiently accurate to initialize asteroid- and planet-based optical navigation, which would have otherwise been infeasible due to the unknown relative position between the observer and celestial object. The combined navigation sequence is completely autonomous and achieves position and velocity 3σ uncertainties on the order of 1000 km and 1m/s. Variable stars can also provide velocity information along a line of sight, which is known as range-rate. An initial orbit determination algorithm is developed that uses sequential range-rate measurements, which is more generalized than existing velocity-based initial orbit determination techniques. The new algorithm enables initial orbit determination with a single sensor capable of measuring range-rate --- such as an X-ray detector measuring pulsar frequency Doppler shifts using a digital phase-locked loop --- as opposed to a minimum of three sensors pointing in different directions needed for synthesizing velocity vector measurements. Furthermore, simulation results suggest that the new method improves navigation accuracy by orders of magnitude in certain orbits compared to existing algorithms due to innovations in the problem formulation. Finally, variable stars are proposed as an aide to star identification during interstellar flight. It is shown that modern star identification techniques are susceptible to false positives and long compute times when the observer is moving at even a small fraction of light speed. Certain variable star populations are easily identifiable by their pulsation profile and brightness, and their density distribution in the sky is proposed as a means for obtaining coarse attitude estimates. The coarse attitude estimate generated by measuring the on-sky density distribution of RR Lyrae variable stars is used to reduce a star catalog used for star identification, which in turn is shown to significantly reduce compute time and false positive rates for a modern star identification algorithm. Together, the body of work in this thesis shows that lost-in-space-and-time state determination can be performed using a wide variety of variable stars with different emission and pulsation properties. Furthermore, variable stars provide many types of measurable data, which are shown to be useful in the determination of numerous spacecraft states not limited to only position. Variable stars can potentially enable or augment spacecraft autonomy in a wide range of mission scenarios, warranting additional investigation in the future.

Graduation Semester

2025-05

Type of Resource

Thesis

Handle URL

https://hdl.handle.net/2142/129423

Copyright and License Information

Copyright 2025 Linyi Hou

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