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

Radar images of humans and other concealed objects are considerably distorted by attenuation, refraction and multipath clutter in indoor through-wall environments. While 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 work 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 two-dimensional array and range processing. The frontal radar images of dynamic targets are simulated using narrowband planar array data processed with two-dimensional 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

Multipath Exploitation-Based Indoor Target Localization Model Using Single Marginal Antenna

Recently, indoor target localization became an area of interest due to its diverse applications. In indoor target localization, surrounding environment creates multipath components, which can be exploited to aid in localization process. A number of studies have been proposed to employ multipath exploitation in localizing indoor targets. However, their localization errors can still be improved. This study proposed a new localization model based on multipath exploitation techniques by using triangulation method. Ultra-wide band signals were resolved and associated using marginal antenna-based scheme. The estimate of the target location was then obtained using measured round-trip time delays. The location was determined by applying the simple trigonometry on the triangle in which real radar, virtual radars, and the target location are the vertices of the triangle in question. Simulation results show that the proposed method has improved the localization error over a wide range of timing errors, target locations and room sizes with the overall maximum localization error of 1.4 m equivalent to 22.2% improvement as compared to 1.8 m localization error obtained using the method developed by the Muqaibel et al. (2017)

Target-to-Target Interaction in Through-the-Wall Radars under Path Loss Compensated Multipath Exploitation-Based Signal Model for Sparse Image Reconstruction

Multipath caused by reflections from interior walls of buildings has been a long-standing challenge that affects Through-the-Wall Radar Imaging. Multipath creates ghost images that introduce confusion when detecting desired targets. Traditionally, multipath exploitation techniques under the compressive sensing framework have widely been applied to address the challenge. However, the multipath component emanating from target-to-target interactions has not been considered–a consequence that may, under multiple target scenarios, lead to incorrect image interpretation. Besides, far targets experience more attenuation due to free space path-loss, hence resulting into target undetectability. This study proposes a signal model, based on multipath exploitation techniques, by designing a sensing matrix that incorporates multipath returns due to target-to-target interaction and path loss compensation. The study, in addition, proposes the path-loss compensator that, if integrated into the proposed signal model, reduces path loss effects. Simulation results show that the Signal to Clutter Ratio and Relative Clutter Peak improved by 4.9 dB and 1.9 dB, respectively compared to the existing model.Keywords: Compressive sensing, multipath ghost, multipath exploitation, pathloss, path-loss compensator, through-the-wall-radar imaging

Through-the-wall radar imaging with compressive sensing; theory, practice and future trends-a review

Through-the-Wall Radar Imaging (TWRI) is anemerging technology which enables us to detect behind the wall targets using electromagnetic signals. TWRI has received considerable attention recently due to its diverse applications. This paper presents fundamentals, mathematical foundations and emerging applications of TWRI with special emphasis on Compressive Sensing (CS) and sparse image reconstruction.Multipath propagation stemming from the surrounding walls and nearby targets are among the impinging challenges.Multipath components produce replicas of the genuine target, ghosts, during image reconstruction which may significantly increase the probability of false alarm. The resulting ghost not only creates confusion with genuine targets but may deteriorate the performance of (CS) algorithms as described in this article. The results from a practical scenario show a promising future of the technology which can be adopted in real-life problems including rescue missions and military purposes.AKey words: spect dependence, compressive sensing, multipath ghost, multipath exploitation, through-the-wall-radar imaging

Through-the-Wall Imaging and Multipath Exploitation

We consider the problem of using electromagnetic sensing to estimate targets in complex environments, such as when they are hidden behind walls and other opaque objects. The often unknown electromagnetic interactions between the target and the surrounding area, make the problem challenging. To improve our results, we exploit information in the multipath of the objects surrounding both the target and the sensors. First, we estimate building layouts by using the jump-diffusion algorithm and employing prior knowledge about typical building layouts. We also take advantage of a detailed physical model that captures the scattering by the inner walls and efficiently utilizes the frequency bandwidth. We then localize targets hidden behind reinforced concrete walls. The sensing signals reflected from the targets are significantly distorted and attenuated by the embedded metal bars. Using the surface formulation of the method of moments, we model the response of the reinforced walls, and incorporate their transmission coefficients into the beamforming method to achieve better estimation accuracy. In a related effort, we utilize the sparsity constraint to improve electromagnetic imaging of hidden conducting targets, assuming that a set of equivalent sources can be substituted for the targets. We derive a linear measurement model and employ l1 regularization to identify the equivalent sources in the vicinity of the target surfaces. The proposed inverse method reconstructs the target shape in one or two steps, using single-frequency data. Our results are experimentally verified. Finally, we exploit the multipath from sensor-array platforms to facilitate direction finding. This in contrast to the usual approach, which utilizes the scattering close to the targets. We analyze the effect of the multipath in a statistical signal processing framework, and compute the Cramer-Rao bound to obtain the system resolution. We conduct experiments on a simple array platform to support our theoretical approach

Sparsity-based autoencoders for denoising cluttered radar signatures

Narrowband and broadband indoor radar images significantly deteriorate in the presence of target-dependent and target-independent static and dynamic clutter arising from walls. A stacked and sparse denoising autoencoder (StackedSDAE) is proposed for mitigating the wall clutter in indoor radar images. The algorithm relies on the availability of clean images and the corresponding noisy images during training and requires no additional information regarding the wall characteristics. The algorithm is evaluated on simulated Doppler-time spectrograms and high-range resolution profiles generated for diverse radar frequencies and wall characteristics in around-the-corner radar (ACR) scenarios. Additional experiments are performed on range-enhanced frontal images generated from measurements gathered from a wideband radio frequency imaging sensor. The results from the experiments show that the StackedSDAE successfully reconstructs images that closely resemble those that would be obtained in free space conditions. Furthermore, the incorporation of sparsity and depth in the hidden layer representations within the autoencoder makes the algorithm more robust to low signal-to-noise ratio (SNR) and label mismatch between clean and corrupt data during training than the conventional single-layer DAE. For example, the denoised ACR signatures show a structural similarity above 0.75 to clean free space images at SNR of −10 dB and label mismatch error of 50%