Autofocus-Based Optimization of Trajectory Filtering for Close-Range ISAR Imaging

Mergenthaler P, Künzle C, Fröhlich AC, Ullmann I, Vossiek M (2026)

Publication Type: Conference contribution, Conference Contribution

Publication year: 2026

URI: https://ieeexplore.ieee.org/document/11674968

DOI: 10.1109/RadarConf2663773.2026.11674968

Abstract

Radar-based imaging of the plantar foot surface enables contactless monitoring of deformation and swelling during gait. This work presents an inverse synthetic aperture radar (ISAR) imaging approach that integrates trajectory estimation from an Azure Kinect depth camera. As the raw depth estimates contain noise and jitter, a filtering and optimization framework is introduced to enhance reconstruction quality. A discrete Kalman filter with constant-acceleration dynamics is optimized using autofocus metrics that quantify image homogeneity and edge sharpness. The feedback loop automatically tunes filter parameters based on the reconstructed image quality rather than trajectory error. Experimental results with a moving 3D-printed foot model demonstrate that the proposed method significantly improves focus and surface consistency compared to unfiltered or root-mean-square-error (RMSE)-based filtering. These findings establish a foundation for contactless, radar-based medical imaging of the plantar foot in dynamic conditions.

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How to cite

APA:

Mergenthaler, P., Künzle, C., Fröhlich, A.-C., Ullmann, I., & Vossiek, M. (2026). Autofocus-Based Optimization of Trajectory Filtering for Close-Range ISAR Imaging. In Proceedings of the 2026 IEEE Radar Conference (RadarConf'26).

MLA:

Mergenthaler, Peter, et al. "Autofocus-Based Optimization of Trajectory Filtering for Close-Range ISAR Imaging." Proceedings of the 2026 IEEE Radar Conference (RadarConf'26) 2026.

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