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Ground Reflection-Aided TomoSAR Imaging with 5G NR Signals

Qiuyuan Yang, Cunhua Pan, Hong Ren, Jiangzhou Wang

Abstract

Tomographic synthetic aperture radar (TomoSAR) enables three-dimensional imaging by resolving targets along the elevation dimension, which is essential for environment reconstruction and infrastructure monitoring. A critical challenge in TomoSAR is the severe multipath propagation that causes ghost targets, range offsets, and elevation ambiguities. To address this, this paper proposes an enhanced Newtonized orthogonal matching pursuit (NOMP) algorithm to extract the delay, Doppler, and complex amplitude parameters of each propagation path, effectively separating line-of-sight (LoS) and multipath components prior to TomoSAR processing. Additionally, a height fusion strategy combining TomoSAR estimates with LoS-ground reflection delay-based inversion improves elevation accuracy. Simulation results demonstrate that the proposed method achieves improved positioning and elevation accuracy while effectively suppressing multipath-induced artifacts.

Ground Reflection-Aided TomoSAR Imaging with 5G NR Signals

Abstract

Tomographic synthetic aperture radar (TomoSAR) enables three-dimensional imaging by resolving targets along the elevation dimension, which is essential for environment reconstruction and infrastructure monitoring. A critical challenge in TomoSAR is the severe multipath propagation that causes ghost targets, range offsets, and elevation ambiguities. To address this, this paper proposes an enhanced Newtonized orthogonal matching pursuit (NOMP) algorithm to extract the delay, Doppler, and complex amplitude parameters of each propagation path, effectively separating line-of-sight (LoS) and multipath components prior to TomoSAR processing. Additionally, a height fusion strategy combining TomoSAR estimates with LoS-ground reflection delay-based inversion improves elevation accuracy. Simulation results demonstrate that the proposed method achieves improved positioning and elevation accuracy while effectively suppressing multipath-induced artifacts.

Paper Structure

This paper contains 11 sections, 38 equations, 4 figures, 2 tables.

Table of Contents

  1. Introduction
  2. SYSTEM MODEL
  3. Wireless Frame Structure
  4. TomoSAR Receiving Signal Model
  5. Algorithm Design
  6. Coarse Estimation
  7. Newton-based Refinement
  8. Ground reflection-based inversion
  9. Computational Complexity Analysis
  10. SIMULATION RESULTS
  11. Conclusion

Figures (4)

  • Figure 1: Frame structure in 5G NR.
  • Figure 2: Monostatic OFDM TomoSAR geometry.
  • Figure 3: Imaging results. (a) SAR image with ground phase. (b) SAR image after ground phase removal. (c) True 3D target distribution. (d) Reconstruction result.
  • Figure 4: RMSE Performance Comparison.