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

Representation of Radar Micro-Dopplers Using Customized Dictionaries

Human motions give rise to frequency modulations, known as micro-Dopplers, to continuous wave radar signals. Micro-Doppler signals have been extensively researched for the classification of different types of human motions as well as to distinguish humans from other moving targets. However, there are two main scenarios where the performance of existing algorithms deteriorates significantly—one, when the channel consists of multiple moving targets resulting in distorted signatures, and two, when the systems conditions during the training stage deviate significantly from the conditions during the test stage. In this chapter, it is demonstrated that both of these limitations can be overcome by representing the radar data through customized dictionaries, fine-tuned to provide sparser representations of the data, than traditional data-independent dictionaries such as Fourier or wavelets. The performances of the algorithms are evaluated with both simulated and measured radar data gathered from moving humans in indoor line-of-sight conditions

Radar simulation of human activities in non line-of-sight environments

textThe capability to detect, track and monitor human activities behind building walls and other non-line-of-sight environments is an important component of security and surveillance operations. Over the years, both ultrawideband and Doppler based radar techniques have been researched and developed for tracking humans behind walls. In particular, Doppler radars capture some interesting features of the human radar returns called microDopplers that arise from the dynamic movements of the different body parts. All the current research efforts have focused on building hardware sensors with very specific capabilities. This dissertation focuses on developing a physics based Doppler radar simulator to generate the dynamic signatures of complex human motions in nonline-of-sight environments. The simulation model incorporates dynamic human motion, electromagnetic scattering mechanisms, channel propagation effects and radar sensor parameters. Detailed, feature-by-feature analyses of the resulting radar signatures are carried out to enhance our fundamental understanding of human sensing using radar. First, a methodology for simulating the radar returns from complex human motions in free space is presented. For this purpose, computer animation data from motion capture technologies are exploited to describe the human movements. Next, a fast, simple, primitive-based electromagnetic model is used to simulate the human body. The microDopplers of several human motions such as walking, running, crawling and jumping are generated by integrating the animation models of humans with the electromagnetic model of the human body. Next, a methodology for generating the microDoppler radar signatures of humans moving behind walls is presented. This involves combining wall propagation functions derived from the finite-difference time-domain (FDTD) simulation with the free space radar simulations of humans. The resulting hybrid simulator of the human and wall is used to investigate the effects of both homogeneous and inhomogeneous walls on human microDopplers. The results are further corroborated by basic point-scatterer analysis of different wall effects. The wall studies are followed by an analysis of the effects of flat grounds on human radar signatures. The ground effect is modeled using the method of images and a ground reflection coefficient. A suitable Doppler radar testbed is developed in the laboratory for simulation validation. Measured data of different human activities are collected in both line-of-sight and through-wall environments and the resulting microDoppler signatures are compared with the simulation results. The human microDopplers are best observed in the joint timefrequency space. Hence, suitable joint time-frequency transforms are investigated for improving the display and the readability of both simulated and measured spectrograms. Finally, two new Doppler radar paradigms are considered. First, a scenario is considered where multiple, spatially distributed Doppler radars are used to measure the microDopplers of a moving human from different viewing angles. The possibility of using these microDoppler data for estimating the positions of different point scatterers on the human body is investigated. Second, a scenario is considered where multiple Doppler radars are collocated in a two-dimensional (2-D) array configuration. The possibility of generating frontal images of human movements using joint Doppler and 2-D spatial beamforming is considered. The performance of this concept is compared with that of conventional 2-D array processing without Doppler processing.Electrical and Computer Engineerin

Radar Enhanced Multi-Armed Bandit for Rapid Beam Selection in Millimeter Wave Communications

Multi-arm bandit (MAB) algorithms have been used to learn optimal beams for millimeter wave communication systems. Here, the complexity of learning the optimal beam linearly scales with the number of beams, leading to high latency when there are a large number of beams. In this work, we propose to integrate radar with communication to enhance the MAB learning performance by searching only those beams where the radar detects a scatterer. Further, we use radar to distinguish the beams that show mobile targets from those which indicate the presence of static clutter, thereby reducing the number of beams to scan. Simulations show that our proposed radar-enhanced MAB reduces the exploration time by searching only the beams with distinct radar mobile targets resulting in improved throughput.Comment: 5 pages, 6 figure

Investigation of and improvements to a continuous wave radar system for human detection through walls

A low cost CW radar system with a two element receiving array has been under development for detecting indoor movers using the Doppler and direction of arrival information of the targets. This thesis investigates some research issues and possible improvements to the radar system to improve its operation. First when the CW radar is used in a through wall paradigm it is important to study the channel propagation effects through the complex medium of a wall. Hence narrowband and wideband measurements were conducted to understand some of the wall phenomenolgy such as attenuation and polarization dependence of different walls. The second issue investigated is the jamming of the receiver by the strong coupling from the transmitter that prevents the detection of the weak returns from the target. A circularly polarized microstrip array was designed at 2.4 GHz to reduce this coupling in the bistatic configuration of the CW radar. Third, the Doppler tracks of the different targets contain additional microdopplers due to the arm and leg motion. This makes the simultaneous tracking of multiple targets very difficult. Hence data association algorithms based on Nearest Neighbour Standard Filtering (NNSF) and Probabilistic Data Association (PDAF) algorithms using Kalman Filtering were developed to track the Doppler tracks of multiple targets in the presence of the microdoppler features.Electrical and Computer Engineerin

Estimation of Electrical Characteristics of Inhomogeneous Walls Using Generative Adversarial Networks

Through-wall radars are researched and developed for the detection, localization, and tracking of human activities in indoor environments. Electromagnetic wave propagation through walls introduces refraction, attenuation, multipath, and ghost targets in the radar signatures. The estimation of wall characteristics (dielectric profile and thickness) can enable wall effects to be deconvolved from through-wall radar signatures. We use generative adversarial networks (GANs) to estimate wall characteristics from narrowband scattered electric fields on the same side of the wall as the transmitter. We demonstrate that the GANs, consisting of two neural networks configured in an adversarial manner, are capable of solving the highly nonlinear regression problem with limited training data to estimate the dielectric profile and thickness of actual walls up to 95% accuracy based on training with simulated data generated from full-wave solvers

Synthesis of Micro-Doppler Signatures of Human Activities From Different Aspect Angles Using Generative Adversarial Networks

In this paper, we propose to produce synthesized micro-Doppler signatures from different aspect angles through conditional generative adversarial networks (cGANs). Micro-Doppler signatures of non-rigid human body motions vary considerably as a function of the radar's aspect angle. Because the direction of the human motion can be arbitrary, a large volume of training data across diverse aspects is needed for practical target activity classification through machine learning. As measurements can require significant monetary and labor costs, the synthesis of micro-Doppler signatures can be an alternate solution. Therefore, we investigate the feasibility of data augmentation through synthesizing micro-Doppler signatures of human activities from diverse radar aspect angles with input data from a single aspect angle. For the training data, the micro-Doppler radar signatures of 12 human activities are generated from different angles ranging from 0 to 315 degrees, at 45-degree increments, through simulations. For each angle, cGANs are trained to synthesize the micro-Doppler signatures for that specific angle given micro-Doppler signatures from another angle. The output of each model is evaluated by calculating mean-square errors and structural similarity indexes between the synthesized micro-Doppler signatures and the ground-truth ones obtained from simulations. We test three different scenarios, and report the respective results. © 2013 IEEE.1