A Novel 3D Imaging Method for Airborne Downward-Looking Sparse Array SAR Based on Special Squint Model

Three-dimensional (3D) imaging technology based on antenna array is one of the most important 3D synthetic aperture radar (SAR) high resolution imaging modes. In this paper, a novel 3D imaging method is proposed for airborne down-looking sparse array SAR based on the imaging geometry and the characteristic of echo signal. The key point of the proposed algorithm is the introduction of a special squint model in cross track processing to obtain accurate focusing. In this special squint model, point targets with different cross track positions have different squint angles at the same range resolution cell, which is different from the conventional squint SAR. However, after theory analysis and formulation deduction, the imaging procedure can be processed with the uniform reference function, and the phase compensation factors and algorithm realization procedure are demonstrated in detail. As the method requires only Fourier transform and multiplications and thus avoids interpolations, it is computationally efficient. Simulations with point scatterers are used to validate the method

Radar Imaging in Challenging Scenarios from Smart and Flexible Platforms

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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

Remote Sensing

This dual conception of remote sensing brought us to the idea of preparing two different books; in addition to the first book which displays recent advances in remote sensing applications, this book is devoted to new techniques for data processing, sensors and platforms. We do not intend this book to cover all aspects of remote sensing techniques and platforms, since it would be an impossible task for a single volume. Instead, we have collected a number of high-quality, original and representative contributions in those areas

Advanced Signal Processing For Multi-Mission Airborne Radar

With the technological advancement of the 21st century, functions of different radars are being merged. A multi-functional system brings the technology of remote sensing to a wide array of applications while at the same time reduces costs of implementation and operation. Ground-based multi-mission radars have been studied in the past. The airborne counterpart deserves a through study with additional and stringent requirements of cost, size, weight, and power.In this dissertation, multi-mission functions in an airborne radar is performed using modular, software-based architecture. The software-based solution is chosen instead of proposing new hardware, primarily because evaluation, validation, and certification of new hardware is onerous and time consuming. The system implementations are validated using simulations as well as field measurements. The simulations are carried out using Mathworks® Phased Array System Toolbox. The field measurements are performed using an enhanced commercial airborne radar system called Polarimetric Airborne Radar Operating at X-band Version 1 (PARADOX1), which is an X-band, vertically polarized, solid state, pulsed radar.The shortcomings of PARADOX1 originate from small aperture size and low power. Various signal processing algorithms are developed and applied to PARADOX1 data to enhance the data quality. Super-resolution algorithms in range, angle, and Doppler domains, for example, have proven to effectively enhance the spatial resolution. An end-to-end study of single-polarized weather measurements is performed using PARADOX1 measurements. The results are compared with well established ground-based radars. The similarities, differences as well as limitations (of such comparisons) are discussed. Sense and Avoid (SAA) tracking is considered as a core functionality and presented in the context of safe integration of Unmanned Aerial Vehicles (UAV) in national airspace. A "nearly" constant acceleration motion model is used in conjunction with Kalman Filter and Joint Probabilistic Data Association (JPDA) to perform tracking operations. The basic SAA tracking function is validated through simulations as well as field measurements.The field-validations show that a modular, software-based enhancement to an existing radar system is a viable solution in realizing multi-mission functionalities in an airborne radar. The SAA tracking is validated in ground-based tests using an x86 based PC with a generic Linux operating system. The weather measurements from PARADOX1 and the subsequent data quality enhancements show that PARADOX1 data products are comparable to those of existing ground based radars

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

Sensor Signal and Information Processing II

In the current age of information explosion, newly invented technological sensors and software are now tightly integrated with our everyday lives. Many sensor processing algorithms have incorporated some forms of computational intelligence as part of their core framework in problem solving. These algorithms have the capacity to generalize and discover knowledge for themselves and learn new information whenever unseen data are captured. The primary aim of sensor processing is to develop techniques to interpret, understand, and act on information contained in the data. The interest of this book is in developing intelligent signal processing in order to pave the way for smart sensors. This involves mathematical advancement of nonlinear signal processing theory and its applications that extend far beyond traditional techniques. It bridges the boundary between theory and application, developing novel theoretically inspired methodologies targeting both longstanding and emergent signal processing applications. The topic ranges from phishing detection to integration of terrestrial laser scanning, and from fault diagnosis to bio-inspiring filtering. The book will appeal to established practitioners, along with researchers and students in the emerging field of smart sensors processing

Intelligent Circuits and Systems

ICICS-2020 is the third conference initiated by the School of Electronics and Electrical Engineering at Lovely Professional University that explored recent innovations of researchers working for the development of smart and green technologies in the fields of Energy, Electronics, Communications, Computers, and Control. ICICS provides innovators to identify new opportunities for the social and economic benefits of society.　 This conference bridges the gap between academics and R&D institutions, social visionaries, and experts from all strata of society to present their ongoing research activities and foster research relations between them. It provides opportunities for the exchange of new ideas, applications, and experiences in the field of smart technologies and finding global partners for future collaboration. The ICICS-2020 was conducted in two broad categories, Intelligent Circuits & Intelligent Systems and Emerging Technologies in Electrical Engineering

Adaptive OFDM Radar for Target Detection and Tracking

We develop algorithms to detect and track targets by employing a wideband orthogonal frequency division multiplexing: OFDM) radar signal. The frequency diversity of the OFDM signal improves the sensing performance since the scattering centers of a target resonate variably at different frequencies. In addition, being a wideband signal, OFDM improves the range resolution and provides spectral efficiency. We first design the spectrum of the OFDM signal to improve the radar\u27s wideband ambiguity function. Our designed waveform enhances the range resolution and motivates us to use adaptive OFDM waveform in specific problems, such as the detection and tracking of targets. We develop methods for detecting a moving target in the presence of multipath, which exist, for example, in urban environments. We exploit the multipath reflections by utilizing different Doppler shifts. We analytically evaluate the asymptotic performance of the detector and adaptively design the OFDM waveform, by maximizing the noncentrality-parameter expression, to further improve the detection performance. Next, we transform the detection problem into the task of a sparse-signal estimation by making use of the sparsity of multiple paths. We propose an efficient sparse-recovery algorithm by employing a collection of multiple small Dantzig selectors, and analytically compute the reconstruction performance in terms of the $ell_1$-constrained minimal singular value. We solve a constrained multi-objective optimization algorithm to design the OFDM waveform and infer that the resultant signal-energy distribution is in proportion to the distribution of the target energy across different subcarriers. Then, we develop tracking methods for both a single and multiple targets. We propose an tracking method for a low-grazing angle target by realistically modeling different physical and statistical effects, such as the meteorological conditions in the troposphere, curved surface of the earth, and roughness of the sea-surface. To further enhance the tracking performance, we integrate a maximum mutual information based waveform design technique into the tracker. To track multiple targets, we exploit the inherent sparsity on the delay-Doppler plane to develop an computationally efficient procedure. For computational efficiency, we use more prior information to dynamically partition a small portion of the delay-Doppler plane. We utilize the block-sparsity property to propose a block version of the CoSaMP algorithm in the tracking filter