Spectral estimation model for linear displacement and vibration monitoring with GBSAR system

Hosseiny, Benyamin; Amini, Jalal; Aghababaei, Hossein

doi:10.1016/j.ymssp.2023.110916

Cited by 1 publication

(1 citation statement)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Unlike satellite or airborne SAR systems, it is typically used for applications which require stable and accurate measurements such as surface deformation, dam and landslide monitoring, stability assessment. and other cases [3][4][5][6][7]. Additionally, GBSAR systems offer space for significant parameter optimization, depending on whether the goal is system efficiency, accuracy, or something else.…”

Section: Introductionmentioning

confidence: 99%

Utilizing Polarization Diversity in GBSAR Data-Based Object Classification

Turčinović,

Kačan,

Bojanjac

et al. 2024

Sensors

View full text Add to dashboard Cite

In recent years, the development of intelligent sensor systems has experienced remarkable growth, particularly in the domain of microwave and millimeter wave sensing, thanks to the increased availability of affordable hardware components. With the development of smart Ground-Based Synthetic Aperture Radar (GBSAR) system called GBSAR-Pi, we previously explored object classification applications based on raw radar data. Building upon this foundation, in this study, we analyze the potential of utilizing polarization information to improve the performance of deep learning models based on raw GBSAR data. The data are obtained with a GBSAR operating at 24 GHz with both vertical (VV) and horizontal (HH) polarization, resulting in two matrices (VV and HH) per observed scene. We present several approaches demonstrating the integration of such data into classification models based on a modified ResNet18 architecture. We also introduce a novel Siamese architecture tailored to accommodate the dual input radar data. The results indicate that a simple concatenation method is the most promising approach and underscore the importance of considering antenna polarization and merging strategies in deep learning applications based on radar data.

show abstract