Advanced Techniques for Ground Penetrating Radar Imaging

Ground penetrating radar (GPR) has become one of the key technologies in subsurface sensing and, in general, in non-destructive testing (NDT), since it is able to detect both metallic and nonmetallic targets. GPR for NDT has been successfully introduced in a wide range of sectors, such as mining and geology, glaciology, civil engineering and civil works, archaeology, and security and defense. In recent decades, improvements in georeferencing and positioning systems have enabled the introduction of synthetic aperture radar (SAR) techniques in GPR systems, yielding GPR–SAR systems capable of providing high-resolution microwave images. In parallel, the radiofrequency front-end of GPR systems has been optimized in terms of compactness (e.g., smaller Tx/Rx antennas) and cost. These advances, combined with improvements in autonomous platforms, such as unmanned terrestrial and aerial vehicles, have fostered new fields of application for GPR, where fast and reliable detection capabilities are demanded. In addition, processing techniques have been improved, taking advantage of the research conducted in related fields like inverse scattering and imaging. As a result, novel and robust algorithms have been developed for clutter reduction, automatic target recognition, and efficient processing of large sets of measurements to enable real-time imaging, among others. This Special Issue provides an overview of the state of the art in GPR imaging, focusing on the latest advances from both hardware and software perspectives

Design and Implementation of a Stepped Frequency Continuous Wave Radar System for Biomedical Applications

There is a need to detect vital signs of human (e.g., the respiration and heart-beat rate) with noncontact method in a number of applications such as search and rescue operation (e.g. earthquakes, fire), health monitoring of the elderly, performance monitoring of athletes Ultra-wideband radar system can be utilized for noncontact vital signs monitoring and tracking of various human activities of more than one subject. Therefore, a stepped-frequency continuous wave radar (SFCW) system with wideband performance is designed and implemented for Vital signs detection and fall events monitoring. The design of the SFCW radar system is firstly developed using off-the-shelf discrete components. Later, the system is implemented using surface mount components to make it portable with low cost. The measurement result is proved to be accurate for both heart rate and respiration rate detection within ±5% when compared with contact measurements. Furthermore, an electromagnetic model has been developed using a multi-layer dielectric model of the human subject to validate the experimental results. The agreement between measured and simulated results is good for distances up to 2 m and at various subjects’ orientations with respect to the radar, even in the presence of more than one subject. The compressive sensing (CS) technique is utilized to reduce the size of the acquired data to levels significantly below the Nyquist threshold. In our demonstration, we use phase information contained in the obtained complex high-resolution range profile (HRRP) to derive the motion characteristics of the human. The obtained data has been successfully utilized for non-contact walk, fall and limping detection and healthcare monitoring. The effectiveness of the proposed method is validated using measured results

Noncontact Vital Signs Detection

Human health condition can be accessed by measurement of vital signs, i.e., respiratory rate (RR), heart rate (HR), blood oxygen level, temperature and blood pressure. Due to drawbacks of contact sensors in measurement, non-contact sensors such as imaging photoplethysmogram (IPPG) and Doppler radar system have been proposed for cardiorespiratory rates detection by researchers.The UWB pulse Doppler radars provide high resolution range-time-frequency information. It is bestowed with advantages of low transmitted power, through-wall capabilities, and high resolution in localization. However, the poor signal to noise ratio (SNR) makes it challenging for UWB radar systems to accurately detect the heartbeat of a subject. To solve the problem, phased-methods have been proposed to extract the phase variations in the reflected pulses modulated by human tiny thorax motions. Advance signal processing method, i.e., state space method, can not only be used to enhance SNR of human vital signs detection, but also enable the micro-Doppler trajectories extraction of walking subject from UWB radar data.Stepped Frequency Continuous Wave (SFCW) radar is an alternative technique useful to remotely monitor human subject activities. Compared with UWB pulse radar, it relieves the stress on requirement of high sampling rate analog-to-digital converter (ADC) and possesses higher signal-to-noise-ratio (SNR) in vital signs detection. However, conventional SFCW radar suffers from long data acquisition time to step over many frequencies. To solve this problem, multi-channel SFCW radar has been proposed to step through different frequency bandwidths simultaneously. Compressed sensing (CS) can further reduce the data acquisition time by randomly stepping through 20% of the original frequency steps.In this work, SFCW system is implemented with low cost, off-the-shelf surface mount components to make the radar sensors portable. Experimental results collected from both pulse and SFCW radar systems have been validated with commercial contact sensors and satisfactory results are shown

FPGA Implementation of Real-Time Compressive Sensing with Partial Fourier Dictionary

This paper presents a novel real-time compressive sensing (CS) reconstruction which employs high density field-programmable gate array (FPGA) for hardware acceleration. Traditionally, CS can be implemented using a high-level computer language in a personal computer (PC) or multicore platforms, such as graphics processing units (GPUs) and Digital Signal Processors (DSPs). However, reconstruction algorithms are computing demanding and software implementation of these algorithms is extremely slow and power consuming. In this paper, the orthogonal matching pursuit (OMP) algorithm is refined to solve the sparse decomposition optimization for partial Fourier dictionary, which is always adopted in radar imaging and detection application. OMP reconstruction can be divided into two main stages: optimization which finds the closely correlated vectors and least square problem. For large scale dictionary, the implementation of correlation is time consuming since it often requires a large number of matrix multiplications. Also solving the least square problem always needs a scalable matrix decomposition operation. To solve these problems efficiently, the correlation optimization is implemented by fast Fourier transform (FFT) and the large scale least square problem is implemented by Conjugate Gradient (CG) technique, respectively. The proposed method is verified by FPGA (Xilinx Virtex-7 XC7VX690T) realization, revealing its effectiveness in real-time applications

Study on THz Imaging System for Concealed Threats Detection.

PhD ThesisMany research groups have conducted studies on Terahertz technology for various applications in the last decades. THz imaging for personnel screening is one prospective application due in part to its superior performance compared with imaging microwave bands. Because of the demand for the accurate detection, it is desirable to devise a high-performance THz imaging system for concealed threats detection. Therefore, this thesis presents my research on the low-cost THz imaging system for security detection. The key contributions of this research lie in investigating the linear sparse periodic array (SPA) THz imaging system for concealed threats detection, improving the traditional reconstruction algorithm of Generalized Synthetic Aperture Focusing Technique (GSAFT) to suppress the ghost images and applying the compressive sensing technique into the proposed SPA-THz imaging system to reduce the sampling data but maintain the image quality. The first part of the work is to investigate the linear sparse periodic array (SPA) and its configuration with large element spacing in simulation, deriving the design guideline for such a SPA THz imaging system. Meanwhile, the improved GSAFT reconstruction algorithm and multi-pass interferometric synthetic aperture imaging technique have been proposed to suppress the ghost image and improve the image quality, respectively. Secondly, the compressive sensing technique has been investigated to reduce the sampling data. Therefore, we have proposed the corresponding discrete CS SPA-THz reconstruction model and verified it in simulation. Finally, we have devised a simplified experimental set-up to assess the practical imaging performance, verifying the proposed SPA-THz imaging system. The set-up only uses 1 Tx and 1 Rx scanning on two separate tracks to effectively realize the proposed imaging system. The reconstructed images by the GSAFT and CS approaches with the measured data have both shown good consistency with the simulated results, respectively. And the multi-pass interferometric synthetic aperture imaging has been experimentally proved effective in improving image SNR and contras

スパースアレイ・レーダにおける信号処理技術

Tohoku University佐藤源之課

Resolving Measurement Errors Inherent with Time-of-Flight Range Imaging Cameras

Range imaging cameras measure the distance to objects in the field-of-view (FoV) of the camera, these cameras enable new machine vision applications in robotics, manufacturing, and human computer interaction. Time-of-flight (ToF) range cameras operate by illuminating the scene with amplitude modulated continuous wave (AMCW) light and measuring the phase difference between the emitted and reflected modulation envelope. Currently ToF range cameras suffer from measurement errors that are highly scene dependent, and these errors limit the accuracy of the depth measurement. The major cause of measurement errors is multiple propagation paths from the light source to pixel, known as multi path interference. Multi-path interference typically arises from: inter reflections, lens flare, subsurface scattering, volumetric scattering, and translucent objects. This thesis contributes three novel methods for resolving multi-path interference: coding in time, coding in frequency, and coding in space. Time coding is implemented by replacing the single frequency amplitude modulation with a binary sequence. Fundamental to ToF range cameras is the cross-correlation between the reflected light and a reference signal. The measured cross-correlation depends on the selection of the binary sequence. With selection of an appropriate binary sequence and using sparse deconvolution on the measured cross-correlation the multiple return path lengths and their amplitudes can be recovered. However, the minimal resolvable path length is dependent on the highest frequency in the binary sequence. Frequency coding is implemented by taking multiple measurements at different modulation frequencies. A subset of frequency coding is operating the camera in a mode analogous to stepped frequency continuous wave (SFCW). Frequency coding uses techniques from radar to resolve multiple propagation paths. The minimal resolvable path length is dependent on the camera's modulation bandwidth and the spectrum estimation technique used to recover distance, and it is shown that SFCW can be used to measure depth of objects behind a translucent sheet, while AMCW measurements can not. Path lengths below quarter a wavelength of the highest modulation frequency are difficult to resolve. The use of spatial coding is used to resolve diffuse multi-path interference. The original technique comes from direct and global separation in computer graphics, and it is modified to operate on the complex data produced by a ToF range camera. By illuminating the scene with a pattern the illuminated areas contain the direct return and the scattering (global return). The non-illuminated regions contain the scattering return, assuming the global component is spatially smooth. The direct and global separation with sinusoidal patterns is combining with the sinusoidal modulation signal of ToF range cameras for a closed form solution to multi-path interference in nine frames. With nine raw frames it is possible to implement direct and global separation at video frame rates. The RMSE of a corner is reduced from 0.0952 m to 0.0112 m. Direct and global separation correctly measures the depth of a diffuse corner, and resolves subsurface scattering however fails to resolve specular reflections. Finally the direct and global separation is combined with replacing the illumination and reference signals with a binary sequence. The combination allows for resolving diffuse multi-path interference present in a corner, with the sparse multi-path interference caused mixed pixels between the foreground and background. The corner is correctly measured and the number of mixed pixels is reduced by 90%. With the development of new methods to resolve multi-path interference ToF range cameras can measure scenes with more confidence. ToF range cameras can be built into small form factors as they require a small number of parts: a pixel array, a light source and a lens. The small form factor coupled with accurate range measurements allows ToF range cameras to be embedded in cellphones and consumer electronic devices, enabling wider adoption and advantages over competing range imaging technologies

Investigating Key Techniques to Leverage the Functionality of Ground/Wall Penetrating Radar

Ground penetrating radar (GPR) has been extensively utilized as a highly efficient and non-destructive testing method for infrastructure evaluation, such as highway rebar detection, bridge decks inspection, asphalt pavement monitoring, underground pipe leakage detection, railroad ballast assessment, etc. The focus of this dissertation is to investigate the key techniques to tackle with GPR signal processing from three perspectives: (1) Removing or suppressing the radar clutter signal; (2) Detecting the underground target or the region of interest (RoI) in the GPR image; (3) Imaging the underground target to eliminate or alleviate the feature distortion and reconstructing the shape of the target with good fidelity. In the first part of this dissertation, a low-rank and sparse representation based approach is designed to remove the clutter produced by rough ground surface reflection for impulse radar. In the second part, Hilbert Transform and 2-D Renyi entropy based statistical analysis is explored to improve RoI detection efficiency and to reduce the computational cost for more sophisticated data post-processing. In the third part, a back-projection imaging algorithm is designed for both ground-coupled and air-coupled multistatic GPR configurations. Since the refraction phenomenon at the air-ground interface is considered and the spatial offsets between the transceiver antennas are compensated in this algorithm, the data points collected by receiver antennas in time domain can be accurately mapped back to the spatial domain and the targets can be imaged in the scene space under testing. Experimental results validate that the proposed three-stage cascade signal processing methodologies can improve the performance of GPR system