A Norm-Minimization Algorithm for Solving the Cold-Start Problem with XNAV
Linyi Hou, Zachary R. Putnam

TL;DR
This paper introduces a norm-minimization algorithm for solving the spacecraft cold-start problem using X-ray pulsar observations, improving computational efficiency and incorporating complex pulsar signal models to enhance navigation accuracy.
Contribution
The paper extends Lohan's banded-error intersection model to 3D space with reduced compute time and includes higher-fidelity pulsar models for improved navigation in cold-start scenarios.
Findings
Algorithm can identify spacecraft position within a 10 AU x 10 AU x 0.01 AU domain.
Median position error is approximately 15 km.
Including parallax, time dilation, and higher-order models is essential for accuracy.
Abstract
An algorithm is presented for solving the cold-start problem using observations of X-ray pulsars. Using a norm-minimization-based approach, the algorithm extends Lohan's banded-error intersection model to 3-dimensional space while reducing compute time by an order of magnitude. Higher-fidelity X-ray pulsar signal models, including the parallax effect, Shapiro delay, time dilation, and higher-order pulsar timing models, are considered. The feasibility of solving the cold-start problem using X-ray pulsar navigation is revisited with the improved models and prior knowledge requirements are discussed. Monte Carlo simulations are used to establish upper bounds on uncertainty and determine the accuracy of the algorithm. Results indicate that it is necessary to account for the parallax effect, time dilation, and higher-order pulsar timing models in order to successfully determine the position…
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Taxonomy
TopicsPulsars and Gravitational Waves Research · Geophysics and Gravity Measurements · GNSS positioning and interference
