Mitigation of through-wall interference in radar images using denoising autoencoders

The detection and identification of humans and concealed objects by through wall radars is affected by wall propagation effects such as attenuation and multipath. Several works, in the past, have provided solutions for mitigating wall effects based on either prior information of the wall parameters or signal processing solutions for separating wall interference from the direct signal from the target to the radar. In this paper, we propose a machine learning based method-denoising autoencoders-to mitigate wall interference effects and for reconstructing an image resembling the ground truth in free space conditions. This method relies on training the algorithm to denoise corrupted through-wall radar images into clean line-of-sight images. We have demonstrated the effectiveness of the proposed solution using simulated narrowband Doppler-Azimuth images in free space and through-wall conditions. We simulated the propagation through diverse wall conditions using stochastic finite difference time domain techniques. Next, we tested the algo­rithm on measured frontal (Azimuth-Elevation) images obtained from Walabot — a wideband, low power, radar with a planar antenna array. Both the measurement and simulation results showed a low error between the denoised reconstructed images and the clean line-of-sight images.