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

• Published in: IEEE transactions on geoscience and remote sensing Vol. 58; no. 9; pp. 6650 - 6663
• Main Authors: Vishwakarma, Shelly, Ram, Shobha Sundar
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.09.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Aerodynamics; Attenuation; Broadband; Clutter; Denoising autoencoders; Doppler radar; Doppler sonar; Doppler/range-enhanced frontal imaging; Indoor environments; Learning algorithms; Machine learning; Measurement; Mitigation; Narrowband; Noise reduction; Propagation; Radar; Radar imaging; Simulation; stochastic finite difference time-domain (sFDTD); Target recognition; through-wall radar; Training; Wall effects
• ISSN: 0196-2892; 1558-0644
• DOI: 10.1109/TGRS.2020.2978440

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.