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Autofocus Method for Human-Body Imaging under Respiratory Motion Using Synthetic Aperture Radar

Masaya Kato, Takuya Sakamoto

TL;DR

This paper tackles the challenge of SAR imaging of the human body under respiratory motion, where motion-induced phase errors blur images. It introduces a hybrid autofocus framework that first separates echoes from different body parts in the range-angle domain, then disambiguates overlapping echoes in the time-frequency domain using a mixture-model and EM-based optimization, and finally estimates per-target phase corrections by maximizing Muller-Buffington sharpness. By integrating per-part focused images, the method achieves a well-focused whole-body SAR image and improves scattering-point localization; experiments with four participants show a 5.1× gain in MB sharpness and a reduction of RMS scattering-point error from 34 mm to 20 mm. This approach advances privacy-preserving, high-precision radar imaging under complex physiological motion, enabling more reliable non-contact physiological and structural assessment.

Abstract

This study presents an effective autofocusing approach for synthetic aperture radar imaging of the human body under conditions of respiratory motion. The proposed method suppresses respiratory-motion-induced phase errors by separating radar echoes in the spatial- and time-frequency domains and estimating phase errors individually for each separated echo. By compensating for the estimated phase errors, synthetic aperture radar images focused on all scattering points are generated, even when multiple body parts exhibit different motions due to respiration. The performance of the proposed method is evaluated through experiments with four participants in the supine position. Compared with a conventional method, the proposed approach improves image quality by a factor of 5.1 in terms of Muller-Buffington sharpness, and reduces the root-mean-square error with respect to a reference point cloud from 34 mm to 20 mm.

Autofocus Method for Human-Body Imaging under Respiratory Motion Using Synthetic Aperture Radar

TL;DR

This paper tackles the challenge of SAR imaging of the human body under respiratory motion, where motion-induced phase errors blur images. It introduces a hybrid autofocus framework that first separates echoes from different body parts in the range-angle domain, then disambiguates overlapping echoes in the time-frequency domain using a mixture-model and EM-based optimization, and finally estimates per-target phase corrections by maximizing Muller-Buffington sharpness. By integrating per-part focused images, the method achieves a well-focused whole-body SAR image and improves scattering-point localization; experiments with four participants show a 5.1× gain in MB sharpness and a reduction of RMS scattering-point error from 34 mm to 20 mm. This approach advances privacy-preserving, high-precision radar imaging under complex physiological motion, enabling more reliable non-contact physiological and structural assessment.

Abstract

This study presents an effective autofocusing approach for synthetic aperture radar imaging of the human body under conditions of respiratory motion. The proposed method suppresses respiratory-motion-induced phase errors by separating radar echoes in the spatial- and time-frequency domains and estimating phase errors individually for each separated echo. By compensating for the estimated phase errors, synthetic aperture radar images focused on all scattering points are generated, even when multiple body parts exhibit different motions due to respiration. The performance of the proposed method is evaluated through experiments with four participants in the supine position. Compared with a conventional method, the proposed approach improves image quality by a factor of 5.1 in terms of Muller-Buffington sharpness, and reduces the root-mean-square error with respect to a reference point cloud from 34 mm to 20 mm.
Paper Structure (18 sections, 20 equations, 17 figures, 3 tables)

This paper contains 18 sections, 20 equations, 17 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Synthetic Aperture Radar Processing for Targets in Motion
  3. Synthetic Aperture Radar Imaging
  4. Phase Compensation for Targets in Motion
  5. Image Sharpness and Phase Optimization
  6. Spectrogram Modeling for Targets with Respiratory Motion
  7. Doppler Frequency Modeling of Respiratory Motion
  8. Modeling of the Spectrogram of the Received Signal
  9. Proposed Autofocusing Method
  10. Echo Separation in the Range-Angle Domain
  11. Echo Separation in the Time--Frequency Domain
  12. Image Generation Method
  13. Experimental Performance Evaluation
  14. Details of the Radar Measurement Experiment
  15. Reference Data Used for Performance Evaluation
  16. ...and 3 more sections

Figures (17)

  • Figure 1: Measured radar image and three local maxima.
  • Figure 2: Weighting function in the range--angle domain.
  • Figure 4: Measured spectrogram.
  • Figure 5: Estimated spectrogram model.
  • Figure 6: Weighting function in the time--frequency domain.
  • ...and 12 more figures