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Mitigation of Through-Wall Distortions of Frontal Radar Images Using Denoising Autoencoders.

Authors :
Vishwakarma, Shelly
Ram, Shobha Sundar
Source :
IEEE Transactions on Geoscience & Remote Sensing. Sep2020, Vol. 58 Issue 9, p6650-6663. 14p.
Publication Year :
2020

Abstract

Radar images of humans and other concealed objects are considerably distorted by attenuation, refraction, and multipath clutter in indoor through-wall environments. Although several methods have been proposed for removing target-independent static and dynamic clutter, there still remain considerable challenges in mitigating target-dependent clutter especially when the knowledge of the exact propagation characteristics or analytical framework is unavailable. In this article, we focus on mitigating wall effects using a machine learning-based solution—denoising autoencoders—that does not require prior information of the wall parameters or room geometry. Instead, the method relies on the availability of a large volume of training radar images gathered in through-wall conditions and the corresponding clean images captured in line-of-sight conditions. During the training phase, the autoencoder learns how to denoise the corrupted through-wall images in order to resemble the free space images. We have validated the performance of the proposed solution for both static and dynamic human subjects. The frontal radar images of static targets are obtained by processing wideband planar array measurement data with 2-D array and range processing. The frontal radar images of dynamic targets are simulated using narrowband planar array data processed with 2-D array and Doppler processing. In both simulation and measurement processes, we incorporate considerable diversity in the target and propagation conditions. Our experimental results, from both simulation and measurement data, show that the denoised images are considerably more similar to the free-space images when compared to the original through-wall images. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
01962892
Volume :
58
Issue :
9
Database :
Academic Search Index
Journal :
IEEE Transactions on Geoscience & Remote Sensing
Publication Type :
Academic Journal
Accession number :
146512580
Full Text :
https://doi.org/10.1109/TGRS.2020.2978440