Kalman Filtering and RTS Smoothing for Arc-Jet Sample Edge Tracking

Alexandre M Quintart; Magnus A Haw

NASA NTRS · 20260008410

Kalman Filtering and RTS Smoothing for Arc-Jet Sample Edge Tracking

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Abstract

Accurate and temporally consistent measurements of test-article recession are required to characterize material response during arc-jet ablation experiments. However, image-based boundary measurements are often affected by segmentation noise, brightness variations, and frame-to-frame variability. This work extends arcjetCV [1] with filtering methods for tracking the evolving boundaries of hemispherical and ISO-Q test articles. Two boundary-tracking approaches based on Kalman filtering [2] were implemented. The first applies a point-wise Kalman filter followed by a Rauch–Tung–Striebel smoother [3] to individual boundary-point locations. The second applies the same filtering and smoothing framework to a reduced set of geometry-dependent shape parameters. The point-wise method reduces local frame-to-frame fluctuations while preserving spatial details along the detected boundary. The shape-parameter method provides a compact and geometrically constrained estimate of the sample contour. Figure 1 illustrates the two approaches for a hemispherical test article. Both methods improve temporal consistency and support more robust estimation of surface recession from arc-jet imagery. The two filtering approaches provide complementary representations of boundary evolution and improve the reliability of image-based recession measurements during arc-jet experiments.

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recession measurement boundary tracking Rauch–Tung–Striebel smoother Kalman filter arc-jet testing thermal protection systems ablation Database arcjet BEAST

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Alexandre M Quintart, Magnus A Haw. Kalman Filtering and RTS Smoothing for Arc-Jet Sample Edge Tracking. https://ntrs.nasa.gov/citations/20260008410

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Ablation subject BEAST subject Database subject Kalman filter subject arc-jet testing subject arcjet subject boundary tracking subject

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