Ground Reflection-Aided TomoSAR Imaging with 5G NR Signals

TL;DR

This paper introduces a novel method combining an enhanced NOMP algorithm and height fusion strategy to improve 3D imaging accuracy in TomoSAR by effectively mitigating multipath effects using 5G NR signals.

Contribution

It presents a new approach that enhances multipath separation and elevation accuracy in TomoSAR imaging with 5G signals, addressing ghost targets and ambiguities.

Findings

Improved positioning accuracy demonstrated in simulations.

Effective suppression of multipath-induced artifacts.

Enhanced elevation resolution compared to traditional methods.

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.