Radar Imaging in Challenging Scenarios from Smart and Flexible Platforms

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Coherence-factor-based rough surface clutter suppression for forward-looking GPR imaging

We present an enhanced imaging procedure for suppression of the rough surface clutter arising in forward-looking ground-penetrating radar (FL-GPR) applications. The procedure is based on a matched filtering formulation of microwave tomographic imaging, and employs coherence factor (CF) for clutter suppression. After tomographic reconstruction, the CF is first applied to generate a "coherence map" of the region in front of the FL-GPR system illuminated by the transmitting antennas. A pixel-by-pixel multiplication of the tomographic image with the coherence map is then performed to generate the clutter-suppressed image. The effectiveness of the CF approach is demonstrated both qualitatively and quantitatively using electromagnetic modeled data of metallic and plastic shallow-buried targets

Novel Methods in Computational Imaging with Applications in Remote Sensing

This dissertation is devoted to novel computational imaging methods with applications in remote sensing. Computational imaging methods are applied to three distinct applications including imaging and detection of buried explosive hazards utilizing array radar, high resolution imaging of satellites in geosynchronous orbit utilizing optical hypertelescope arrays, and characterization of atmospheric turbulence through multi-frame blind deconvolution utilizing conventional optical digital sensors. The first application considered utilizes a radar array employed as a forward looking ground penetrating radar system with applications in explosive hazard detection. A penalized least squares technique with sparsity-inducing regularization is applied to produce imagery, which is consistent with the expectation that objects are sparsely populated but extended with respect to the pixel grid. Additionally, a series of pre-processing steps is demonstrated which result in a greatly reduced data size and computational cost. Demonstrations of the approach are provided using experimental data and results are given in terms of signal to background ratio, image resolution, and relative computation time. The second application involves a sparse-aperture telescope array configured as a hypertelescope with applications in long range imaging. The penalized least squares technique with sparsity-inducing regularization is adapted and applied to this very different imaging modality. A comprehensive study of the algorithm tuning parameters is performed and performance is characterized using the Structure Similarity Metric (SSIM) to maximize image quality. Simulated measurements are used to show that imaging performance achieved using the pro- posed algorithm compares favorably in comparison to conventional Richardson-Lucy deconvolution. The third application involves a multi-frame collection from a conventional digital sensor with the primary objective of characterizing the atmospheric turbulence in the medium of propagation. In this application a joint estimate of the image is obtained along with the Zernike coefficients associated with the atmospheric PSF at each frame, and the Fried parameter r0 of the atmosphere. A pair of constraints are applied to a penalized least squares objective function to enforce the theoretical statistics of the set of PSF estimates as a function of r0. Results of the approach are shown with both simulated and experimental data and demonstrate excellent agreement between the estimated r0 values and the known or measured r0 values respectively

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

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

Through-The-Wall Detection Using Ultra Wide Band Frequency Modulated Interrupted Continuous Wave Signals

Through-The-Wall-Detection (TTWD) techniques can improve the situational awareness of police and soldiers, and support first responders in search and rescue operations. A variety of systems for TTWD based on different waveforms have been developed and presented in the literature, e.g. radar systems based on pulses, noise or pseudo-noise waveforms, and frequency modulated continuous wave (FMCW) or stepped frequency continuous wave (SFCW) waveforms. Ultra wide band signals are normally used as they provide suitable resolution to discriminate different targets. A common problem for active radar systems for TTWD is the strong backscattered signal from the air-wall interface. This undesired signal can overshadow the reflections from actual targets, especially those with low radar cross section like human beings, and limit the dynamic range at the receiver, which could be saturated and blocked. Although several techniques have been developed to address this problem, frequency modulated interrupted continuous wave (FMICW) waveforms represent an interesting further approach to wall removal, which can be used as an alternative technique or combined with the existing ones. FMICW waveforms have been used in the past for ionospheric and ocean sensing radar systems, but their application to the wall removal problem in TTWD scenarios is novel. The validation of the effectiveness of the proposed FMICW waveforms as wall removal technique is therefore the primary objective of this thesis, focusing on comparing simulated and experimental results using normal FMCW waveforms and using the proposed FMICW waveforms. Initially, numerical simulations of realistic scenarios for TTWD have been run and FMICW waveforms have been successfully tested for different materials and internal structure of the wall separating the radar system and the targets. Then a radar system capable of generating FMICW waveforms has been designed and built to perform a measurement campaign in environments of the School of Engineering and Computing Sciences, Durham University. These tests aimed at the localization of stationary targets and at the detection of people behind walls. FMICW waveforms prove to be effective in removing/mitigating the undesired return caused by antenna cross-talk and wall reflections, thus enhancing the detection of targets

FMCW Radar signal processing for Antarctic Ice Shelf profiling and imaging

This thesis contains details of all the signal processing work being done on FMCW Radar (operating at VHF-UHF band) for the Antarctic Ice Shelf monitoring project that has been carried out at UCL. The system developed at UCL was based on a novel concept of phase-sensitive FMCW radar with low power consumption, thus allowing data collection for long period of time with millimetre range precision. Development of new signal processing method was required in order to process the large amount of data, along with the signal processing technique for obtaining the high precision range values. This was achieved during the first stage of the thesis, providing accurate ice shelf basal layer melt rate values. Properties of the FMCW radar system and experimental scenarios posed further signal processing challenges. Those challenges were met by developing number of novel algorithms. A novel shape matching algorithm was developed to detect internal layers underneath the ice shelf. Range migration correction method was developed to compensate for the defocusing of the image in large angles due to high fractional bandwidth of the radar system. Vertical error correction method was developed to compensate for any vertical displacement of the radar antenna during field experiment. Finally, a novel 3-D MIMO imaging algorithm for the Antarctic ice shelf base study was developed. This was done to process the 8x8 MIMO radar (developed at UCL) data. The radars have been deployed in the Antarctica during the Austral summer of each year from 2011-2014. The field experiments were done in the Ronne, Larsen-C, Larsen North, George VI and Ross ice shelves. The novel signal processing techniques have been successfully applied on the real data, allowing better understanding of the Antarctic ice shelf features

Convex Model-Based Synthetic Aperture Radar Processing

The use of radar often conjures up images of small blobs on a screen. But current synthetic aperture radar (SAR) systems are able to generate near-optical quality images with amazing benefits compared to optical sensors. These SAR sensors work in all weather conditions, day or night, and provide many advanced capabilities to detect and identify targets of interest. These amazing abilities have made SAR sensors a work-horse in remote sensing, and military applications. SAR sensors are ranging instruments that operate in a 3D environment, but unfortunately the results and interpretation of SAR images have traditionally been done in 2D. Three-dimensional SAR images could provide improved target detection and identification along with improved scene interpretability. As technology has increased, particularly regarding our ability to solve difficult optimization problems, the 3D SAR reconstruction problem has gathered more interest. This dissertation provides the SAR and mathematical background required to pose a SAR 3D reconstruction problem. The problem is posed in a way that allows prior knowledge about the target of interest to be integrated into the optimization problem when known. The developed model is demonstrated on simulated data initially in order to illustrate critical concepts in the development. Then once comprehension is achieved the processing is applied to actual SAR data. The 3D results are contrasted against the current gold- standard. The results are shown as 3D images demonstrating the improvement regarding scene interpretability that this approach provides

Colocated MIMO radar using compressive sensing

We propose the use of compressive sensing (CS) in the context of a multi-input multioutput (MIMO) radar system that is implemented by a small scale network. Each receive node compressively samples the incoming signal, and forwards a small number of samples to a fusion center. At the fusion center, all received data are jointly processed to extract information on the potential targets via the CS approach. Since CS-based MIMO radar would require many fewer measurements than conventional MIMO radar for reliable target detection, there would be power savings during the data transmission to the fusion center, which would prolong the life of the wireless network. First, we propose a direction of arrival (DOA)-Doppler estimation approach. Assuming that the targets are sparsely located in the DOA-Doppler space, based on the samples forwarded by the receive nodes, the fusion center formulates an ℓ1-optimization problem, the solution of which yields the target DOA-Doppler information. The proposed approach achieves the superior resolution of MIMO radar with far fewer samples than required by conventional approaches. Second, we propose the use of step frequency to CS-based MIMO radar, which enables high range resolution, while transmitting narrowband pulses. For slowly moving targets, a novel approach is proposed that achieves significant complexity reduction by successively estimating angle-range and Doppler in a decoupled fashion and by employing initial estimates to further reduce the search space. Numerical results show that the achieved complexity reduction does not hurt resolution. Finally, we investigate optimal designs for the measurement matrix that is used to linearly compress the received signal. One optimality criterion amounts to decorrelating the bases that span the sparse space of the incoming signal and simultaneously enhancing signal-to-interference ratio (SIR). Another criterion targets SIRimprovement only. It is shown via simulations that, in certain cases, the measurement matrices obtained based on the aforementioned criteria can improve detection accuracy as compared to the typically used Gaussian random measurement matrix.Ph.D., Electrical Engineering -- Drexel University, 201