Physical model-driven deep networks for through-the-wall radar imaging

Article Geochemistry & Geophysics

Clutter Removal in Through-the-Wall Radar Imaging Using Sparse Autoencoder With Low-Rank Projection

Fok Hing Chi Tivive et al.

Summary: The article proposes a learning-based approach to reduce the impact of wall clutter and background noise, using a sparse autoencoder with a low-rank projection. Experiments using real data from a stepped-frequency radar demonstrate the effectiveness of the method in removing wall clutter, showing superior performance compared to existing approaches.

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2021)

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Article Geochemistry & Geophysics

Human Target Detection Based on FCN for Through-the-Wall Radar Imaging

Huquan Li et al.

Summary: In this letter, an adaptive target detection method based on fully convolutional network (FCN) is proposed to address the challenges of shape variance, image overlap, and weak scattering target detection in through-the-wall radar imaging. The method utilizes a downsampling-upsampling structure to extract multiscale features and integrates an attention mechanism with the FCN for improved weak scattering target detection. The algorithm is evaluated through simulation and experimental tests, demonstrating better detection performance compared to existing methods.

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS (2021)

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Article Computer Science, Information Systems

Joint Compressed Sensing and Spread Spectrum Through-the-Wall Radar Imaging

Minchao Li et al.

Summary: This study introduces compressed sensing (CS) technology to spread spectrum through-the-wall radar imaging, reducing the number of observation points and samples per point, and significantly decreasing the amount of data required for imaging while achieving satisfactory results.

IEEE ACCESS (2021)

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Article Engineering, Electrical & Electronic