Fast GO/PO RCS calculation: A GO/PO parallel algorithm implemented on GPU and accelerated using a BVH data structure and the Type 3 Non-Uniform FFT

The purpose of this PhD research was to develop and optimize a fast numeric algorithm able to compute monostatic and bistatic RCS predictions obtaining an accuracy comparable to what commercially available from well-known electromagnetic CADs, but requiring unprecedented computational times. This was realized employing asymptotic approximated methods to solve the scattering problem, namely the Geometrical Optics (GO) and the Physical Optics (PO) theories, and exploiting advanced algorithmical concepts and cutting-edge computing technology to drastically speed-up the computation. The First Chapter focuses on an historical and operational overview of the concept of Radar Cross Section (RCS), with specific reference to aeronautical and maritime platforms. How geometries and materials influence RCS is also described. The Second Chapter is dedicated to the first phase of the algorithm: the electromagnetic field transport phase, where the GO theory is applied to implement the “ray tracing”. In this Chapter the first advanced algorithmical concept which was adopted is described: the Bounding Volume Hierarchy (BVH) data structure. Two different BVH approaches and their combination are described and compared. The Third Chapter is dedicated to the second phase of the calculation: the radiation integral, based on the PO theory, and its numerical optimization. Firstly the Type-3 Non-Uniform Fast Fourier Transform (NUFFT) is presented as the second advanced algorithmical tool that was used and it was indeed the foundation of the calculation of the radiation integral. Then, to improve the performance but also to make the application of the approach feasible in case of electrically large objects, the NUFFT was further optimized using a “pruning” technique, which is a stratagem used to save memory and computational time by avoiding calculating points of the transformed domain that are not of interest. To validate the algorithm, a preliminary measurement campaign was held at the headquarter of the Ingegneria Dei Sistemi (IDS) Company, located in Pisa. The measurements, performed on canonical scatterers using a Synthetic Aperture Radar (SAR) imaging equipment set up on a planar scanner inside a semi-anechoic chamber, are discussed

SARCASTIC v2.0 - High-performance SAR simulation for next-generation ATR systems

Synthetic aperture radar has been a mainstay of the remote sensing field for many years, with a wide range of applications across both civilian and military contexts. However, the lack of openly available datasets of comparable size and quality to those available for optical imagery has severely hampered work on open problems such as automatic target recognition, image understanding and inverse modelling. This paper presents a simulation and analysis framework based on the upgraded SARCASTIC v2.0 engine, along with a selection of case studies demonstrating its application to well-known and novel problems. In particular, we demonstrate that SARCASTIC v2.0 is capable of supporting complex phase-dependent processing such as interferometric height extraction whilst maintaining near-realtime performance on complex scenes

Uso de arquitecturas MIC para la aceleración de soluciones numéricas en electromagnetismo

La mejora en la eficiencia de recursos computacionales para la resolución de problemas electromagnéticos es un tema complejo y de gran interés. La aparición en la última década de GPUs y tarjetas coprocesadoras Xeon Phi en las listas de los supercomputadores con mayor rendimiento, ha llevado a los investigadores a tratar de sacar el máximo provecho de estas nuevas tecnologías. El objetivo principal de esta Tesis es mejorar la eficiencia del método MoM (Method of Moments) mediante la paralelización de algunos de sus algoritmos en procesadores con arquitectura Intel MIC (Many Integrated Core). Para ello, se realiza el modelado de un problema electromagnético mediante la metodología SIE-MoM (Surface Integral Equation-Method of Moments), y se desarrollan nuevos algoritmos para su ejecución en tarjetas coprocesadoras Intel Xeon Phi. Los resultados obtenidos tras evaluar los tiempos de computación comparativamente entre las tarjetas Intel Xeon Phi y las CPUs Intel Xeon, indican que la arquitectura Intel MIC podría resultar adecuada en simulaciones electromagnéticas como complemento a CPUs.Improving the efficiency of computational resources for solving electromagnetic problems is a complex subject of great interest. The growth of GPUs (Graphics Processing Unit) and Xeon Phi coprocessor boards on the lists of top-performing supercomputers over the past decade has led researchers to try to make the most of these new technologies. The main objective of this Thesis is to improve the efficiency of the MoM method by parallelizing some of its algorithms on processors with Intel MIC (Many Integrated Core) architecture. For this purpose, the modeling of an electromagnetic problem is carried out using the SIE-MoM (Surface Integral Equation-Method of Moments) methodology, and new algorithms are developed for their execution on Intel Xeon Phi coprocessor cards. The results obtained after evaluating computation time compared between Intel Xeon Phi cards and Intel Xeon CPUs, indicate that the Intel MIC architecture could be suitable in electromagnetic simulations as a complement to CPUs

Autonomous Vehicles: MMW Radar Backscattering Modeling of Traffic Environment, Vehicular Communication Modeling, and Antenna Designs

77 GHz Millimeter-wave (mmWave) radar serves as an essential component among many sensors required for autonomous navigation. High-fidelity simulation is indispensable for nowadays’ development of advanced automotive radar systems because radar simulation can accelerate the design and testing process and help people to better understand and process the radar data. The main challenge in automotive radar simulation is to simulate the complex scattering behavior of various targets in real time, which is required for sensor fusion with other sensory simulation, e.g. optical image simulation. In this thesis, an asymptotic method based on a fast-wideband physical optics (PO) calculation is developed and applied to get high fidelity radar response of traffic scenes and generate the corresponding radar images from traffic targets. The targets include pedestrians, vehicles, and other stationary targets. To further accelerate the simulation into real time, a physics-based statistical approach is developed. The RCS of targets are fit into statistical distributions, and then the statistical parameters are summarized as functions of range and aspect angles, and other attributes of the targets. For advanced radar with multiple transmitters and receivers, pixelated-scatterer statistical RCS models are developed to represent objects as extend targets and relax the requirement for far-field condition. A real-time radar scene simulation software, which will be referred to as Michigan Automotive Radar Scene Simulator (MARSS), based on the statistical models are developed and integrated with a physical 3D scene generation software (Unreal Engine 4). One of the major challenges in radar signal processing is to detect the angle of arrival (AOA) of multiple targets. A new analytic multiple-sources AOA estimation algorithm that outperforms many well-known AOA estimation algorithms is developed and verified by experiments. Moreover, the statistical parameters of RCS from targets and radar images are used in target classification approaches based on machine learning methods. In realistic road traffic environment, foliage is commonly encountered that can potentially block the line-of-sight link. In the second part of the thesis, a non-line-of-sight (NLoS) vehicular propagation channel model for tree trunks at two vehicular communication bands (5.9 GHz and 60 GHz) is proposed. Both near-field and far-field scattering models from tree trunk are developed based on modal expansion and surface current integral method. To make the results fast accessible and retractable, a macro model based on artificial neural network (ANN) is proposed to fit the path loss calculated from the complex electromagnetic (EM) based methods. In the third part of the thesis, two broadband (bandwidth > 50%) omnidirectional antenna designs are discussed to enable polarization diversity for next-generation communication systems. The first design is a compact horizontally polarized (HP) antenna, which contains four folded dipole radiators and utilizing their mutual coupling to enhance the bandwidth. The second one is a circularly polarized (CP) antenna. It is composed of one ultra-wide-band (UWB) monopole, the compact HP antenna, and a dedicatedly designed asymmetric power divider based feeding network. It has about 53% overlapping bandwidth for both impedance and axial ratio with peak RHCP gain of 0.9 dBi.PHDElectrical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/163001/1/caixz_1.pd

Adaptation of an acoustic propagation model to the parallel architecture of a graphics processor

High performance underwater acoustic models are of great importance for enabling real-time acoustic source tracking, geoacoustic inversion, environmental monitoring and high-frequency underwater communications. Given the parallelizable nature of raytracing, in general, and of the ray superposition algorithm in particular, use of multiple computing units for the development of real-time e cient applications based on ray tracing is becoming of extreme importance.Fundação para a Ciência e Tecnologi

Internet of Underwater Things and Big Marine Data Analytics -- A Comprehensive Survey

The Internet of Underwater Things (IoUT) is an emerging communication ecosystem developed for connecting underwater objects in maritime and underwater environments. The IoUT technology is intricately linked with intelligent boats and ships, smart shores and oceans, automatic marine transportations, positioning and navigation, underwater exploration, disaster prediction and prevention, as well as with intelligent monitoring and security. The IoUT has an influence at various scales ranging from a small scientific observatory, to a midsized harbor, and to covering global oceanic trade. The network architecture of IoUT is intrinsically heterogeneous and should be sufficiently resilient to operate in harsh environments. This creates major challenges in terms of underwater communications, whilst relying on limited energy resources. Additionally, the volume, velocity, and variety of data produced by sensors, hydrophones, and cameras in IoUT is enormous, giving rise to the concept of Big Marine Data (BMD), which has its own processing challenges. Hence, conventional data processing techniques will falter, and bespoke Machine Learning (ML) solutions have to be employed for automatically learning the specific BMD behavior and features facilitating knowledge extraction and decision support. The motivation of this paper is to comprehensively survey the IoUT, BMD, and their synthesis. It also aims for exploring the nexus of BMD with ML. We set out from underwater data collection and then discuss the family of IoUT data communication techniques with an emphasis on the state-of-the-art research challenges. We then review the suite of ML solutions suitable for BMD handling and analytics. We treat the subject deductively from an educational perspective, critically appraising the material surveyed.Comment: 54 pages, 11 figures, 19 tables, IEEE Communications Surveys & Tutorials, peer-reviewed academic journa

Computational Electromagnetics Applied to Scattering Observed by Polarimetric Weather Radar

The primary topics of this dissertation are issues existing in the current ensemble scattering procedures. These procedures are failing to quantitatively reproduce polarimetric signatures from resolution volumes filled with ensembles of resonant size precipitation, biota, and anthropogenic scatterers. Sources of these failures are traced to the constraints on the topology that is admissible to the different modeling procedures. The dissertation evaluates in a systematic manner the current modeling procedures focusing on limitation sources and their effects on the overall process of polarimetric variable simulation. It re-evaluates limitations of the widely used T-Matrix approach and discusses sources of instability. Based on the identified limitations, a novel computational electromagnetics (CEM) approach to scatterer modeling and polarimetric variable calculation is introduced to mitigate the current limitations. Detailed overview of the process as well as guidance on applying the CEM to the polarimetric variable calculation is presented. This is the first systematic exploration of a specific CEM solver to modeling of polarimetric radar signatures from precipitation and biota. Finally, to demonstrate meteorological application the CEM approach is evaluated by comparison with some polarimetric radar observations of hail. Of main significance is modeling of large and giant hail having surface protuberances, or rough, irregular shape. Additionally, radar observations of biota and radar cross section (RCS) measurements are considered for aeroecology applications. As an example, the precise size and shape model of Brazilian Free-tailed bat (Tadarida brasiliensis) is created and compared to the RCS measurements, as well as to radar observations of bat emergence in Texas plains