Vishwakarma, S;
Ram, SS;
(2020)
Mitigation of Through-Wall Distortions of Frontal Radar Images Using Denoising Autoencoders.
IEEE Transactions on Geoscience and Remote Sensing
, 58
(9)
pp. 6650-6663.
10.1109/TGRS.2020.2978440.
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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.
| Type: | Article |
|---|---|
| Title: | Mitigation of Through-Wall Distortions of Frontal Radar Images Using Denoising Autoencoders |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/TGRS.2020.2978440 |
| Publisher version: | https://doi.org/10.1109/TGRS.2020.2978440 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Radar imaging, Clutter, Noise reduction, Training, Doppler radar, Aerodynamics |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Security and Crime Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10113412 |
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