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

Low Complexity Algorithm for Range-Point Migration-Based Human Body Imaging for Multistatic UWB Radars

High-resolution, short-range sensors that can be applied in optically challenging environments (e.g., in the presence of clouds, fog, and/or dark smog) are in high demand for various applications. Ultrawideband radar is a promising sensor that is suitable for short-range surveillance or watching sensors. Range-point migration (RPM) has been recently established as a promising imaging approach to achieve accurate and real-time 3-D imaging. However, when objects with many scattering points are dealt with, such as a human body, RPM suffers from high computational costs. In this letter, we propose an algorithm with a lower complexity for an RPM-based 3-D imaging method by introducing a sampling-based scattering center extraction with a simplified evaluation function, in which an efficient sample pattern is provided by a golden ratio. The results from a finite-difference time-domain-based numerical test, which introduces a realistic human body object, demonstrate that our proposed method remarkably reduces the computational cost without sacrificing the reconstruction accuracy

Design and application of microstrip leaky wave antennas for radar sensing

textThis dissertation investigates the application of the frequency-scanned beam of a microstrip leaky wave antenna (LWA) to track humans in the two-dimensional (2-D) range-azimuth plane. The history, operating principles and frequency-scanned properties of a microstrip LWA are first reviewed. The basic concept of using a microstrip LWA to track humans is verified by designing, building and testing a broadband microstrip LWA, developing the necessary processing algorithm, and collecting data using a vector network analyzer. A number of topics are then investigated to further advance the concept. First, the idea of combining the frequency-scanned antenna with a short-pulse ultra-wideband (UWB) radar is developed to realize a portable, real-time system for human tracking. The radar concept and the components of the system are discussed in detail. Line-of-sight and through-wall measurements of a human subject are carried out to demonstrate the performance. Second, a new LWA structure is proposed to achieve a narrower azimuth beam, which requires both a small leaky-wave attenuation constant and a long aperture. The transverse resonance method (TRM) is applied to analyze the proposed structure and the results are verified with measurements of a built prototype. Third, a new signal processing technique, compressive sensing, is applied to further improve the resolution in both the azimuth and down range dimensions. The technique is tested with simulation and measurement data and is shown to produce sharper target responses in both the down range and azimuth dimensions. Lastly, the radar cross-section (RCS) of a microstrip LWA is studied. The antenna mode scattering and structural mode scattering are modeled separately. A ray picture is provided to explain the observed time-domain features using the group delay of the leaky wave.Electrical and Computer Engineerin

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%

Land Cover Classification using Sentinel-1 Radar Mission Interferometry

Synthetic Aperture Radar (SAR) has been widely used for many years in the field of remote sensing. SAR has valuable contribution due to its ability to provide complementary information to optical systems, penetration of radar waves through volumetric targets and high-resolution. SAR has the ability to operate during day and night. It provides operational services under all weather conditions. SAR imagery has many applications including land cover changes, environmental monitoring, climate change and military surveillance. This work focuses on land cover classification with SAR interferometry (InSAR) technique using Sentinel-1 space radar image pair. Sentinel-1 data were collected over the southern part of Estonia. Two SLC SAR images were acquired from both Sentinel-1A and Sentinel-1B with six days temporal difference. In this study, interferometric coherence and backscattering intensity processing chains have been set up and applied to Sentinel-1 SAR image pair. The Sentinel Application Platform (SNAP) has been used for processing of single pair for Sentinel-1 mission. The SNAP is an European Space Agency (ESA) software. The Sentinel-1 image pair processing has been done using Sentinel-1 Toolbox (S1TBX) which is a part of SNAP. Corine Land Cover (CLC) 2012 database has been used as a reference data with 20 m resolution. The CLC2012 contains land use/cover information for most of the European countries. A single optical image from Sentinel-2A was additionally used for feature extraction. An overall accuracy of 68% to 73% was achieved when performing classification into five classes (Urban, Field, Forest, Peat-land, Water) using supervised classification with k-nearest neighbour (kNN) algorithm. The accuracy assessment was done by using confusion matrices

Sensors for Vital Signs Monitoring

Sensor technology for monitoring vital signs is an important topic for various service applications, such as entertainment and personalization platforms and Internet of Things (IoT) systems, as well as traditional medical purposes, such as disease indication judgments and predictions. Vital signs for monitoring include respiration and heart rates, body temperature, blood pressure, oxygen saturation, electrocardiogram, blood glucose concentration, brain waves, etc. Gait and walking length can also be regarded as vital signs because they can indirectly indicate human activity and status. Sensing technologies include contact sensors such as electrocardiogram (ECG), electroencephalogram (EEG), photoplethysmogram (PPG), non-contact sensors such as ballistocardiography (BCG), and invasive/non-invasive sensors for diagnoses of variations in blood characteristics or body fluids. Radar, vision, and infrared sensors can also be useful technologies for detecting vital signs from the movement of humans or organs. Signal processing, extraction, and analysis techniques are important in industrial applications along with hardware implementation techniques. Battery management and wireless power transmission technologies, the design and optimization of low-power circuits, and systems for continuous monitoring and data collection/transmission should also be considered with sensor technologies. In addition, machine-learning-based diagnostic technology can be used for extracting meaningful information from continuous monitoring data

Emerging Approaches for THz Array Imaging: A Tutorial Review and Software Tool

Accelerated by the increasing attention drawn by 5G, 6G, and Internet of Things applications, communication and sensing technologies have rapidly evolved from millimeter-wave (mmWave) to terahertz (THz) in recent years. Enabled by significant advancements in electromagnetic (EM) hardware, mmWave and THz frequency regimes spanning 30 GHz to 300 GHz and 300 GHz to 3000 GHz, respectively, can be employed for a host of applications. The main feature of THz systems is high-bandwidth transmission, enabling ultra-high-resolution imaging and high-throughput communications; however, challenges in both the hardware and algorithmic arenas remain for the ubiquitous adoption of THz technology. Spectra comprising mmWave and THz frequencies are well-suited for synthetic aperture radar (SAR) imaging at sub-millimeter resolutions for a wide spectrum of tasks like material characterization and nondestructive testing (NDT). This article provides a tutorial review of systems and algorithms for THz SAR in the near-field with an emphasis on emerging algorithms that combine signal processing and machine learning techniques. As part of this study, an overview of classical and data-driven THz SAR algorithms is provided, focusing on object detection for security applications and SAR image super-resolution. We also discuss relevant issues, challenges, and future research directions for emerging algorithms and THz SAR, including standardization of system and algorithm benchmarking, adoption of state-of-the-art deep learning techniques, signal processing-optimized machine learning, and hybrid data-driven signal processing algorithms...Comment: Submitted to Proceedings of IEE

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

Behind-wall target detection using micro-doppler effects

Abstract: During the last decade technology for seeing through walls and through dense vegetation has interested many researchers. This technology offers excellent opportunities for military and police applications, though applications are not limited to the military and police; they go beyond those applications to where detecting a target behind an obstacle is needed. To be able to disclose the location and velocity of obscured targets, scientists’ resort to electromagnetic wave propagation. Thus, through-the-wall radar (TWR) is technology used to propagate electromagnetic waves towards a target through a wall. Though TWR is a promising technology, it has been reported that TWR imaging (TWRI) poses a range of ambiguities in target characterisation and detection. These ambiguities are related to the thickness and electric properties of walls. It has been reported that the mechanical and electric properties of the wall defocus the target image rendered by the radar. The defocusing problem is the phenomenon of displacing the target away from its true location when the image is rendered. Thus, the operator of the TWR will have a wrong position, not the real position of the target. Defocusing is not the only problem observed while the signal is travelling through the wall. Target classification, wall modelling and others are areas that need investigation...D.Ing. (Electrical and Electronic Engineering

Contribution to ground-based and UAV SAR systems for Earth observation

Mankind's way of life is the main driver of a planetary-scale change that is marked by the growing of human population's demand of energy, food, goods, services and information. As a result, it have emerged new ecological, economical, social and geopolitical concerns. In this scenario, SAR remote sensing is a potential tool that provides unique information about the Earth's properties and processes that can be used to solve societal challenges of local and global dimension. SARs, which are coherent systems that are able to provide high resolution images with weather independence, represent a suitable alternative for EO with diverse applications. Some examples of SAR application areas are topography (DEM generation with interferometry), agriculture (crop classification or soil moisture), or geology (monitoring surface deformation). In this framework, the encompassing objective of the present doctoral work has been part of the implementation and the subsequent evaluation of capabilities of two X-band SAR sensors. On the one hand, the RISKSAR-X radar designed to be operated at ground to monitor small-scale areas of observation and, on the other, the ARBRES-X sensor designed to be integrated into small UAVs. Despite its inherently dissimilar conception, the concurrence of both sensors has been evidenced along this manuscript. By taking advantage of the similarities between them, it has been possible to analogously assess both sensors to obtain conclusions. In this context, the common link has been the development of the polarimetric OtF operation mode of the RISKSAR-X, allowing this sensor to be operated equivalently to the ARBRES-X. Regarding the RISKSAR-X SAR sensor, several hardware contributions have been developed during part of this Ph.D. with the aim of improving the system performance. By endowing the system with the capability to operate in the fully polarimetric OtF acquisition mode, the relative long scanning time has been reduced. It is of great interest since the measured scatterers that present a short term variable reflectivity during the scanning time, such as moving vegetation, may degrade the extracted parameters from the retrieved data and the SAR image reconstruction. During this doctoral activity, it has been studied the image blurring, the decorrelation and the coherence degradation introduced by this effect. Furthermore, a new term in the differential interferometric coherence that takes into account the image blurring has been introduced. Concerning the ARBRES-X SAR system, one of the main objectives pursued during this Ph.D. has been the integration of the sensor into a small UAV MP overcoming restrictions of weight, size and aerodynamics of the platform. The use of this type of platforms is expected to open up new possibilities in airborne SAR remote sensing, since it offers much more versatility than the commonly used fixed wings UAVs. Different innovative flight strategies with this type of platforms have been assessed and some preliminary results have been obtained with the use of the ARBRES-X SAR system. During the course of the present doctoral work, much effort has been devoted to achieve the first experimental repeat-pass interfereometric results obtained with the UAV MP together with the ARBRES-X. Moreover, the sensor has been endowed with fully polarimetric capabilities by applying the improvements developed to the RISKSAR-X radar, which is another example of the duality between both systems. Finally, a vertical and a semicircular aperture have been successfully performed obtaining SLC images of the scenario, which envisages the capability of the UAV MP to perform tomographic images and complete circular apertures in the future. In conclusion, the UAV MP is a promising platform that opens new potentials for several applications, such as repeat-pass interferometry or differential tomography imaging with the realization of almost arbitrary trajectories.El mode de viure de la humanitat és el principal motor d'un canvi a escala planetària que està marcat per la creixent demanda d'energia, d'aliment, de béns, de serveis i d'informació de les poblacions humanes. Com a resultat, han sorgit noves inquietuds ecològiques, econòmiques, socials i geopolítiques. En aquest escenari, la detecció remota SAR és una eina potencial que proporciona informació única sobre les propietats i processos de la Terra que es pot utilitzar per resoldre reptes socials de dimensió local i global. Els SARs, que són sistemes coherents que poden proporcionar imatges d'alta resolució amb independència del temps, representen una alternativa adequada per a l'observació de la Terra. Alguns exemples d'àrees d'aplicació SAR són la topografia (generació de DEM amb interferometria), l'agricultura (classificació de cultius o humitat del sòl) o la geologia (monitoratge de deformació superficial). En aquest context, l'objectiu general del present doctorat ha estat part de la implementació i posterior avaluació de les capacitats de dos sensors SAR de banda X. D'una banda, el radar RISKSAR-X dissenyat per funcionar a terra i monitoritzar àrees d'observació a petita escala i, d'altra, el sensor ARBRES-X dissenyat per ser integrat en petits UAVs. Malgrat la seva concepció inherentment diferent, la concurrència d'ambdós sensors s'ha evidenciat al llarg d'aquest manuscrit. Aprofitant les similituds entre ells, s'han pogut avaluar de forma anàloga els dos sensors per obtenir conclusions. En aquest sentit, el vincle comú ha estat el desenvolupament del mode de funcionament polimètric OtF del RISKSAR-X, permetent que aquest sensor operi de forma equivalent a l'ARBRES-X. Pel que fa al sensor RISKSAR-X, s'han desenvolupat diverses contribucions hardware durant part d'aquest doctorat amb l'objectiu de millorar el rendiment del sistema. En dotar el sistema de la possibilitat d'operar en el mode d'adquisició totalment polarimètric OtF, s'ha reduït el relatiu llarg temps d'escaneig. Això és de gran interès ja que els blancs mesurats que presenten una reflectivitat variable a curt termini, com ara la vegetació en moviment, poden degradar els paràmetres extrets de les dades recuperades i la reconstrucció d'imatges SAR. Durant aquesta activitat doctoral s'ha estudiat el desenfocat de la imatge, la decorrelació i la degradació de la coherència introduïts per aquest efecte. A més, s'ha introduït un nou terme en la coherència interferomètrica diferencial que té en compte el desenfocat de la imatge. Pel que fa al sistema ARBRES-X, un dels principals objectius perseguits durant aquest doctorat ha estat la integració del sensor en un petit UAV MP superant les restriccions de pes, grandària i aerodinàmica de la plataforma. S'espera que l'ús d'aquest tipus de plataformes obri noves possibilitats en la detecció remota SAR aerotransportada, ja que ofereix molta més versatilitat que els UAV d'ales fixes habituals. S'han avaluat diferents estratègies de vol innovadores amb aquest tipus de plataformes i s'han obtingut resultats preliminars amb l'ús del sistema ARBRES-X. Durant el transcurs del present treball, s'ha dedicat molt esforç a assolir els primers resultats experimentals d'interferometria de múltiple passada obtinguts amb l'UAV MP conjuntament amb l'ARBRES-X. A més, el sensor ha estat dotat de capacitats totalment polarimètriques aplicant les millores desenvolupades al radar RISKSAR-X, el qual constitueix un altre exemple de la dualitat entre ambdós sistemes. Finalment, s'han realitzat amb èxit una apertura vertical i semicircular obtenint imatges SLC de l'escenari, el qual permet preveure la capacitat de l'UAV MP per a realitzar imatges tomogràfiques i apertures circulars completes en el futur. En conclusió, l'UAV MP és una plataforma prometedora que obre nous potencials per a diverses aplicacions, com ara la interferometria de múltiple passada o la tomografia diferencial amb la realització de trajectòries gairebé arbitràries