Enhanced Through-the-Wall Radar Imaging Based on Deep Layer Aggregation

Qu, Lele; Wang, Chang‐An; Tian-hong, Yang; Zhang, Lili; Sun, Yue

doi:10.1109/lgrs.2022.3171714

Cited by 4 publications

(1 citation statement)

References 13 publications

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“…Neural network methods, renowned for their adaptability, processing capability, and robustness, have been widely used in various industries. Their application in SAR target detection and ghost image suppression has been thoroughly investigated, with research mainly focusing on training networks to distinguish targets from clutter, suppress clutter, and extract targets [13][14][15][16][17][18][19]. However, these methods demand significant data and parameter input and are characterized by high computational complexity.…”

Section: Introductionmentioning

confidence: 99%

SAR Multi-Angle Observation Method for Multipath Suppression in Enclosed Spaces

Lin,

Zhao,

Wang

et al. 2024

Remote Sensing

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Synthetic aperture radar (SAR) is a powerful tool for detecting and imaging targets in enclosed environments, such as tunnels and underground garages. However, SAR performance is degraded by multipath effects, which occur when electromagnetic waves are reflected by obstacles, such as walls, and interfere with the direct signal. This results in the formation of multipath ghost images, which obscure the true target and reduce the image quality. To overcome this challenge, we propose a novel method based on multi-angle observation. This method exploits the fact that the position of ghost images changes depending on the angle of the radar, while the position of the true target remains stable. By collecting and processing multiple data sets from different angles, we can eliminate the ghost images and enhance the target image. In addition, we introduce a center vector distance algorithm to address the complexity and computational intensity of existing multipath suppression algorithms. This algorithm, which defines the primary direction of multi-angle vectors from stable scattering centers as the center vector, processes and synthesizes multiple data sets from multi-angle observations. It calculates the distance of pixel intensity sequences in the composite data image from the center vector. Pixels within a specified threshold are used for imaging, and the final result is obtained. Simulation experiments and real SAR data from underground garages confirm the effectiveness of this method in suppressing multipath ghost images.

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