Deep Network for Simultaneous Decomposition and Classification in UWB-SAR Imagery

Classifying buried and obscured targets of interest from other natural and manmade clutter objects in the scene is an important problem for the U.S. Army. Targets of interest are often represented by signals captured using low-frequency (UHF to L-band) ultra-wideband (UWB) synthetic aperture radar (SAR) technology. This technology has been used in various applications, including ground penetration and sensing-through-the-wall. However, the technology still faces a significant issues regarding low-resolution SAR imagery in this particular frequency band, low radar cross sections (RCS), small objects compared to radar signal wavelengths, and heavy interference. The classification problem has been firstly, and partially, addressed by sparse representation-based classification (SRC) method which can extract noise from signals and exploit the cross-channel information. Despite providing potential results, SRC-related methods have drawbacks in representing nonlinear relations and dealing with larger training sets. In this paper, we propose a Simultaneous Decomposition and Classification Network (SDCN) to alleviate noise inferences and enhance classification accuracy. The network contains two jointly trained sub-networks: the decomposition sub-network handles denoising, while the classification sub-network discriminates targets from confusers. Experimental results show significant improvements over a network without decomposition and SRC-related methods

Collaborative Multi-Robot Search and Rescue: Planning, Coordination, Perception, and Active Vision

Search and rescue (SAR) operations can take significant advantage from supporting autonomous or teleoperated robots and multi-robot systems. These can aid in mapping and situational assessment, monitoring and surveillance, establishing communication networks, or searching for victims. This paper provides a review of multi-robot systems supporting SAR operations, with system-level considerations and focusing on the algorithmic perspectives for multi-robot coordination and perception. This is, to the best of our knowledge, the first survey paper to cover (i) heterogeneous SAR robots in different environments, (ii) active perception in multi-robot systems, while (iii) giving two complementary points of view from the multi-agent perception and control perspectives. We also discuss the most significant open research questions: shared autonomy, sim-to-real transferability of existing methods, awareness of victims' conditions, coordination and interoperability in heterogeneous multi-robot systems, and active perception. The different topics in the survey are put in the context of the different challenges and constraints that various types of robots (ground, aerial, surface, or underwater) encounter in different SAR environments (maritime, urban, wilderness, or other post-disaster scenarios). The objective of this survey is to serve as an entry point to the various aspects of multi-robot SAR systems to researchers in both the machine learning and control fields by giving a global overview of the main approaches being taken in the SAR robotics area

Radar Imaging in Challenging Scenarios from Smart and Flexible Platforms

undefine

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

Machine Learning for Beamforming in Audio, Ultrasound, and Radar

Multi-sensor signal processing plays a crucial role in the working of several everyday technologies, from correctly understanding speech on smart home devices to ensuring aircraft fly safely. A specific type of multi-sensor signal processing called beamforming forms a central part of this thesis. Beamforming works by combining the information from several spatially distributed sensors to directionally filter information, boosting the signal from a certain direction but suppressing others. The idea of beamforming is key to the domains of audio, ultrasound, and radar. Machine learning is the other central part of this thesis. Machine learning, and especially its sub-field of deep learning, has enabled breakneck progress in tackling several problems that were previously thought intractable. Today, machine learning powers many of the cutting edge systems we see on the internet for image classification, speech recognition, language translation, and more. In this dissertation, we look at beamforming pipelines in audio, ultrasound, and radar from a machine learning lens and endeavor to improve different parts of the pipelines using ideas from machine learning. We start off in the audio domain and derive a machine learning inspired beamformer to tackle the problem of ensuring the audio captured by a camera matches its visual content, a problem we term audiovisual zooming. Staying in the audio domain, we then demonstrate how deep learning can be used to improve the perceptual qualities of speech by denoising speech clipping, codec distortions, and gaps in speech. Transitioning to the ultrasound domain, we improve the performance of short-lag spatial coherence ultrasound imaging by exploiting the differences in tissue texture at each short lag value by applying robust principal component analysis. Next, we use deep learning as an alternative to beamforming in ultrasound and improve the information extraction pipeline by simultaneously generating both a segmentation map and B-mode image of high quality directly from raw received ultrasound data. Finally, we move to the radar domain and study how deep learning can be used to improve signal quality in ultra-wideband synthetic aperture radar by suppressing radio frequency interference, random spectral gaps, and contiguous block spectral gaps. By training and applying the networks on raw single-aperture data prior to beamforming, it can work with myriad sensor geometries and different beamforming equations, a crucial requirement in synthetic aperture radar

Enhanced Microwave Imaging of the Subsurface for Humanitarian Demining Applications

© Cranfield University 2020. All rights reserved. No part of this publication may be reproduced without the written permission of the copyright ownerThis thesis presents a theoretical analysis and applied evaluation deploying ground penetrat ing radar (GPR) for landmine detection. An original contribution has been made in designing and manufacturing a light-weight, low-cost, fully polarimetric antenna system for GPR, enabling easy transportation and as sembly. This facilitates extensive use by various smaller communities in remote areas. By achieving the goal of supplying various smaller communities with advanced ground pene trating radar technology the technological standard of landmine detection can be improved beyond existing solutions such as metal detection or manual probing. The novel radar system itself allows detection of various subsurface targets of different shapes and sizes, metallic and non-metallic, in a number of different soils, such as sand, loam or gravel and therefore can be used in versatile environments. The GPR system has been realised by designing novel light-weight, 3D printed X-band horn antennas, manufactured from single piece plastic then copper electroplated. These an tennas are 50% lighter than their commercial equivalents. They are incorporated in an an tenna array as a group of four to allow full-polarimetric imaging of the subsurface. High resolution images of landmines and calibration targets were performed in the subsurface over an experimental sand test bed. For performing subsurface measurements in the near-field, four novel gradient-index (GRIN) lenses were designed and 3D printed to be incorporated in the apertures of the X band antennas. The improved target detection from these lenses was proven by scanning the test bed and comparing the imaging data of the antenna array with and without lenses attached. A rigorous theoretical study of different decomposition techniques and their effect on the imaging and detection accuracy for polarimetric surface penetrating data was performed and applied to the gathered imaging data to reliably isolate and detect subsurface targets. Studied decomposition techniques were Pauli decomposition parameters and Yamaguchi polarime try decomposition. It was found that it is paramount to use both algorithms on one set of subsurface data to detect all features of a buried target. A novel temporal imaging technique was developed for exploiting natural occurring changes in soil moisture level, and hence its dielectric properties. Contrary to the previously intro duced imaging techniques this moisture change detection (MCD) mechanism does not rely on knowledge of the used measurement setup or deploying clutter suppression techniques. This time averaged technique uses several images of a moist subsurface taken over a period while the moisture evaporates from the soil. Each image pixel is weighted by the phase change occurring over the evaporation period and a resulting B-scan image reveals the subsurface targets without surrounding clutter. Finally, a multi-static antenna set-up is examined on its capability for suppressing sur face clutter and its limitations are verified by introducing artificial surface clutter in form of pebbles to the scene. The resulting technique was found to suppress up to 30 The GPR antenna system developed in this thesis and the corresponding imaging tech niques have contributed to a significant improvement in subsurface radar imaging perfor mance and target discrimination capabilities. This work will contribute to more efficient landmine clearance in some of the most challenged parts of the world.Ph

Enhanced microwave imaging of the subsurface for humanitarian demining applications

© Cranfield University 2020. All rights reserved. No part of this publication may be reproduced without the written permission of the copyright ownerThis thesis presents a theoretical analysis and applied evaluation deploying ground penetrating radar (GPR) for landmine detection. An original contribution has been made in designing and manufacturing a light-weight, low-cost, fully polarimetric antenna system for GPR, enabling easy transportation and assembly. This facilitates extensive use by various smaller communities in remote areas. By achieving the goal of supplying various smaller communities with advanced ground penetrating radar technology the technological standard of landmine detection can be improved beyond existing solutions such as metal detection or manual probing. The novel radar system itself allows detection of various subsurface targets of different shapes and sizes, metallic and non-metallic, in a number of different soils, such as sand, loam or gravel and therefore can be used in versatile environments. The GPR system has been realised by designing novel light-weight, 3D printed X-band horn antennas, manufactured from single piece plastic then copper electroplated. These antennas are 50% lighter than their commercial equivalents. They are incorporated in an antenna array as a group of four to allow full-polarimetric imaging of the subsurface. High resolution images of landmines and calibration targets were performed in the subsurface over an experimental sand test bed. For performing subsurface measurements in the near-field, four novel gradient-index (GRIN) lenses were designed and 3D printed to be incorporated in the apertures of the Xband antennas. The improved target detection from these lenses was proven by scanning the test bed and comparing the imaging data of the antenna array with and without lensesattached. A rigorous theoretical study of different decomposition techniques and their effect on the imaging and detection accuracy for polarimetric surface penetrating data was performed and applied to the gathered imaging data to reliably isolate and detect subsurface targets. Studied decomposition techniques were Pauli decomposition parameters and Yamaguchi polarimetry decomposition. It was found that it is paramount to use both algorithms on one set of subsurface data to detect all features of a buried target. A novel temporal imaging technique was developed for exploiting natural occurring changes in soil moisture level, and hence its dielectric properties. Contrary to the previously introduced imaging techniques this moisture change detection (MCD) mechanism does not rely on knowledge of the used measurement setup or deploying clutter suppression techniques. This time averaged technique uses several images of a moist subsurface taken over a period while the moisture evaporates from the soil. Each image pixel is weighted by the phase change occurring over the evaporation period and a resulting B-scan image reveals the subsurface targets without surrounding clutter. Finally, a multi-static antenna set-up is examined on its capability for suppressing surface clutter and its limitations are verified by introducing artificial surface clutter in form of pebbles to the scene. The resulting technique was found to suppress up to 30 The GPR antenna system developed in this thesis and the corresponding imaging techniques have contributed to a significant improvement in subsurface radar imaging performance and target discrimination capabilities. This work will contribute to more efficient landmine clearance in some of the most challenged parts of the world

Summaries of the Sixth Annual JPL Airborne Earth Science Workshop

The Sixth Annual JPL Airborne Earth Science Workshop, held in Pasadena, California, on March 4-8, 1996, was divided into two smaller workshops:(1) The Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) workshop, and The Airborne Synthetic Aperture Radar (AIRSAR) workshop. This current paper, Volume 2 of the Summaries of the Sixth Annual JPL Airborne Earth Science Workshop, presents the summaries for The Airborne Synthetic Aperture Radar (AIRSAR) workshop