Deep learning-based reconstruction for near-field MIMO radar imaging

Date

2023-01-01

Author

Manisali, Irfan
Öktem, Sevinç Figen

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Near-field multiple-input multiple-output (MIMO) radar imaging systems are of interest in diverse fields such as medicine, through-wall imaging, and surveillance. The imaging performance of these systems highly depends on the underlying image reconstruction method. While sparsity-based methods offer better image quality than the traditional direct inversion methods, their high computational cost is undesirable in real-time applications. In this paper, we develop a novel deep learning-based reconstruction method for near-field MIMO radar imaging. The main goal is to achieve high image quality with low computational cost. The developed approach has a two-staged structure. The physics-based first stage performs adjoint operation to back project the measurements to the reconstruction space, and DNN-based second stage converts these backprojected measurements to a scene reflectivity image. As DNN, a 3D U-Net is used to jointly exploit range and cross-range correlations. We illustrate the performance of the reconstruction method using a synthetically generated dataset. The results demonstrate the effectiveness of the developed method compared to commonly used analytical approaches in terms of image quality and computation time.

Subject Keywords

computational imaging, deep learning, inverse problems, microwave imaging, MIMO, radar imaging

URI

https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85175830783&origin=inward
https://hdl.handle.net/11511/107181

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Department of Electrical and Electronics Engineering, Conference / Seminar