<p>Aircrafttrajectory optimization is traditionally used forminimizing fuel consumption or time when going from one flightstate to another. This thesis presents a possible approach toincorporate tactical constraints in aircraft trajectoryoptimization.</p><p>The stealth technology of today focuses on making thetactics already in use more effective. Since tactics andstealth are closely interrelated, new and better results may beobtained if both aspects are considered simultaneously. Simplyreducing the radar cross section area in some directionswithout considering tactical aspects may result in little, ifany, improvement.</p><p>Flight tests have been performed in cooperation withEricsson Microwave Systems and the Swedish Air Force FlightAcademy. The aircraft used was the subsonic jet trainer Saab105, designated SK60 by the Swedish Air Force. The results showa decrease of 40% in the time interval between the instant theaircraft was first detected until it could pass above the radarstation. This corresponds to a reduced radar cross section(RCS) in the direction from the aircraft to the radar of almost90%, if classical RCS reduction techniques would have beenapplied.</p><p>If a modern aircraft with stealth properties would be used,the proposed methodology is believed to increase the possibleimprovements further. This is because the variation of themagnitude of RCS in different directions is greater for a shapeoptimized aircraft, which is the property exploited by thedeveloped method.</p><p>The methods presented are indeed an approach utilizing theideas of the network centric warfare (NCW) concept. Themethodology presented depends on accurate information about theadversary, while also providing up-to-date information to theother users in the information network.</p><p>The thesis focuses on aircraft but the methods are generaland may be adapted for missiles, shipsor land vehicles. Theproposed methods are also economically viable since they areuseful for existing platforms without costly modifications. Themethods presented are not limited to radar threats only. Thereasons for using radar in this thesis are the availablenon-classified data and that radar is known to pose a majorthreat against aircraft.</p>

<p>When simulating target seekers, there is a great need for computationally efficient, target models. This report considers a study of radar target modelling based on Inverse Synthetic Aperture Radar (ISAR) measurements of generic aircraft. The results underlie future modelling of full-size air targets. </p><p>A method is developed for two-dimensional modelling of aspect-dependent target scattering. The approach taken is to generate point-scatterer models of two targets, where each point scatterer is defined according to its position and radar cross section (RCS), estimated from ISAR images. The scattered energy contributions from all point scatterers are summed to simulate a radar return signal. To validate the models, the modelled radar target centre is compared to the true radar target centre, which is determined from ISAR images. </p><p>The method is presented to be promising for modelling air targets with large, persistent radar cross section.</p>

With the ever growing application requirements for wireless power transmission in recent years, use of Ultra High Frequency (UHF) band via passive RFID technology escalates quickly. However, limited read range and outdoor interference has always been a great obstacle for various RFID applications. Escalating power transmission at the tag to identify and amplify received power under flawless conditions of electromagnetic theory do not provide estimates of read-rates, which bring major limitations to RFID system performance. Therefore, discovering the reason behind these problems and assessing the performance of backscatter power to improve the system performance remains as a crying need.   Implying radar cross section (RCS) mechanism into RFID can enhance the system performance at a larger extent, as passive RFID works same as radar at far field range by detecting backscatter signal from target object. Antenna radiation pattern and co located interference effect are vital considerations for RFID propagation mechanism and tag read range optimization. Consequently, the robust performance of transmitting and receiving antenna will provide a better RCS value when we get them in good agreement with experimental results.   This thesis provides analytical framework for backscatter performance modeling and suggest techniques to enhance the efficiency of reader to tag to reader performance. It explores uncertainties associated with certain parameters like antenna far field radiation property, antenna spacing, optimal backscatter power and communication range, which implies scattering efficiency of the tag and establish a relationship between the measured and predicted values of tag read-rate probabilities. Comparing measurement patterns in both outdoor and in an-echoic chamber, finally it determines method to increase efficiency at power transmission and reception end. Obtained results will encourage the future researchers to design, analyze and enhance the backscattered passive RFID systems at a larger scenario.

Current marine navigation radars are capable of high-resolution imagery of marine ice but are not able to classify the marine ice. Classifying marine ice means identifying the ice as first-year ice, multi-year ice or glacier ice. The latter two ice types are as hard as concrete and capable of damaging even ice hardened vessels such as icebreakers. The Canadian Coast Guard has identified the ability of marine navigation radars to classify marine ice as the single greatest improvement to be made in the safety of Arctic navigation. This thesis presents new research that improves our understanding of electromagnetic backscatter from marine ice. The goal of this work was two-fold: to demonstrate the feasibility of using commercial computational electromagnetic modelling software to simulate real-world marine ice targets, and to identify an optimum frequency or range of frequencies at which the marine ice targets can be definitively classified. Engineering models for scattering from electrically large objects made of a highly variable, complex, heterogenous, three-phase mixture of ice, air and brine are developed. To do so, an extensive literature review of the Arctic environment, and the physical and electrical properties of marine ice, is conducted to distill the required geophysical parameters of the three marine ice types of interest in this work. Using well-established dielectric mixing theory, these parameters are applied to homogenize the marine ice and model the target (in the presence of a flat sea halfspace) using a surface integral equation formulation. To reduce the computational resources required to numerically solve the integral equation models using the method of moments, computational electromagnetic modelling studies are conducted to select a suitable seawater halfspace representation and determine if the properties of larger objects can be inferred from scaled down models of the object. A case study is presented for backscatter from marine ice from 6 to 10 GHz, which explores the effects of frequency on the co- and cross-polarized backscatter intensity (and hence the apparent radar cross-section) of the three marine ice types of interest. Good agreement is found between the co- and cross-polarized backscatter intensity responses found from the engineering model computations and some existing experimental data from real-world marine ice targets. This work: (a) proves the feasibility of using computational electromagnetic modelling to simulate real-world marine ice targets, providing a new, cost-effective method for the study of backscatter from marine ice; (b) confirms the viability of using cross-polarization as a method of classification; and (c) identifies 10 to 16 GHz as a potential optimal frequency range for the classification of marine ice using dual-polarization radar.

abstract: Within the past two decades, metasurfaces, with their unique ability to tailor the wavefront, have attracted scientific attention. Along with many other research areas, RADAR cross-section (RCS)-reduction techniques have also benefited from metasurface technology. In this dissertation, a novel technique to synthesize the RCS-reduction metasurfaces is presented. This technique unifies the two most widely studied and two well-established modern RCS-reduction methods: checkerboard RCS-reduction andgradient-index RCS-reduction. It also overcomes the limitations associated with these RCS-reduction methods. It synthesizes the RCS-reduction metasurfaces, which can be juxtaposed with almost any existing metasurface, to reduce its RCS. The proposed technique is fundamentally based on scattering cancellation. Finally, an example of the RCS-reduction metasurface has been synthesized and introduced to reduce the RCS of an existing high-gain metasurface ground plane. After that, various ways of obtaining ultrabroadband RCS-reduction using the same technique are proposed, which overcome the fundamental limitation of the conventional checkerboard metasurfaces, where the reflection phase difference of (180+-37) degrees is required to achieve 10-dB RCS reduction. First, the guideline on how to select Artificial Magnetic Conductors (AMCs) is explained with an example of a blended checkerboard architecture where a 10-dB RCS reduction is observed over 83% of the bandwidth. Further, by modifying the architecture of the blended checkerboard metasurface, the 10-dB RCS reduction bandwidth increased to 91% fractional bandwidth. All the proposed architectures are validated using measured data for fabricated prototypes. Critical steps for designing the ultrabroadband RCS reduction checkerboard surface are summarized. Finally, a broadband technique to reduce the RCS of complex targets is presented. By using the proposed technique, the problem of reducing the RCS contribution from such multiple-bounces simplifies to identifying and implementing a set of orthogonal functions. Robust guidelines for avoiding grating lobes are provided using array theory. The 90 degree dihedral corner is used to verify the proposed technique. Measurements are reported for a fabricated prototype, where a 70% RCS-reduction bandwidth is observed. To generalize the method, a 45 degree dihedral corner, with a quadruple-bounce mechanism, is considered. Generalized guidelines are summarized and applied to reduce the RCS of complex targets using the proposed method. / Dissertation/Thesis / Doctoral Dissertation Electrical Engineering 2020

This diploma thesis is aimed at radar cross section of passenger cars. Basic scattering parameters of simple targets are described. Selected numeric methods used for computing radar cross section are discussed. Shape and size of simplified vehicle models is investigated. Simulations are verified by computing RCS of objects with well-known characteristics. Radar cross section of Skoda Octavia was simulated using selected numeric methods. Results of all performed simulations are shown in included graphs. Experimental measurment of radar cross section of a vehicle was performed so the measured data can be compared with the simulations.

Compressive Sensing (CS) is a novel signal-processing paradigm that allows sampling of sparse or compressible signals at lower than Nyquist rate. The past decade has seen substantial research on imaging applications using compressive sensing. In this thesis, CS is combined with the commercial electromagnetic (EM) simulation software newFASANT to improve its efficiency in solving EM scattering problems such as Radar Cross Section (RCS) of complex targets at GHz frequencies. This thesis proposes a CS-RCS approach that allows efficient and accurate recovery of under-sampled RCSs measured from a random set of incident angles using an accelerated iterative soft thresh-holding reconstruction algorithm. The RCS results of a generic missile and a Canadian KingAir aircraft model simulated using Physical Optics (PO) as the EM solver at various frequencies and angular resolutions demonstrate good efficiency and accuracy of the proposed method.

Satellite radiometers are used to remotely measure properties of the Earth's surface. Radiometers enable wide spatial coverage and daily temporal coverage. Radiometer measurements are used in a wide array of applications, including freeze/thaw states inference, vegetation index calculations, rainfall estimation, and soil moisture estimation. Resolution enhancement of these radiometer measurements enable finer details to be resolved and improve our understanding of Earth. The Soil Moisture Active Passive (SMAP) radiometer was launched in April 2014 with a goal to produce high resolution soil moisture estimates. However, due to hardware failure of the radar channels, prepared algorithms could no longer be used. Current algorithms utilize a narrow spatial and temporal overlap between the SMAP radiometer and the SENTINEL-1 radar to produce high resolution soil moisture estimates that are spatially and temporally limited. This thesis explores the use of resolution enhancing algorithms to produce high resolution soil moisture estimates without the spatial coverage limitations caused by using multiple sensors. Two main approaches are considered: calculating the iterative update in brightness temperature and calculating the update in soil moisture. The best performing algorithm is the Soil Moisture Image Reconstruction (SMIR) algorithm that is a variation of the Radiometer form of the Scatterometer Image Reconstruction (rSIR) algorithm that has been adapted to operate in parameter space. This algorithm utilizes a novel soil moisture measurement response function (SMRF) in the reconstruction. It matches or exceeds the performance of other algorithms and allows for wide spatial coverage.

Scatterometry and radiometry are used to obtain measurements of Earth properties with extensive spatial coverage at daily or near-daily temporal resolution. Their measurements are used in many climate studies and weather applications, such as iceberg tracking, ocean wind estimation, and volumetric soil moisture measurements. The spatial resolution of these data products ranges from a few kilometers to tens of kilometers. Techniques to enhance the spatial resolution of these products help reveal finer scale features, but come at the cost of increased noise. This thesis explores the application of neural networks as a possible method to handle the noise and uncertainty in enhanced resolution scatterometer and radiometer data products. The specific sensors discussed are the Advanced Scatterometer (ASCAT) and its Ultrahigh Resolution (UHR) winds, and the Soil Moisture Active Passive (SMAP) radiometer and its soil moisture measurements. ASCAT UHR winds have already been validated in previous studies [1], but inherent ambiguity in the wind retrieval model couples with higher noise levels to decrease overall accuracy. Neural networks are tested as an alternate modeling method to possibly improve the accuracy compared with the current method. It is found that the feed forward neural networks tested are able to accurately estimate winds in most calculations, but struggle with the same ambiguity that occurs in the current model. The neural networks handle this ambiguity inconsistently, which results in worse overall network performance compared to the current wind retrieval method. For the SMAP soil moisture measurements, the radiometer form of the Scatterometer Image Reconstruction algorithm is validated as a method to enhance resolution. While the increased noise at higher resolution does worsen overall accuracy, the performance remains within about 0.04 cm^3 cm^−3 RMSE of a validated soil moisture product, suggesting that fine scale features revealed as resolution is enhanced are accurate. Corrections to the soil moisture extraction model used in these tests could further improve these results. Neural networks are then applied and compared with the theory-based approach to extract soil moisture from the brightness temperature measurements, and are found to give slightly more accurate results than the theoretical model, though with somewhat higher error variance.

Indiana University-Purdue University Indianapolis (IUPUI) / Driving safety is a very important topic in vehicle development. One of the biggest threat of driving safety is road departure. Many vehicle active safety technologies have been developed to warn and mitigate road departure in recent years. In order to evaluate the performance of road departure warning and mitigation technologies, the standard testing environment need to be developed. The testing environment shall be standardized to provide consistent and repeatable features in various locations worldwide and in various seasons. The testing environment should also be safe to the vehicle under test in case the safety features do not function well. Therefore, soft, durable and reusable surrogates of roadside objects need to be used. Meanwhile, all surrogates should have the same representative characteristics of real roadside objects to di erent automotive sensors (e.g. radar, LIDAR and camera). This thesis describes the study on identifying the radar characteristics of common roadside objects, metal guardrail, grass, and concrete divider, and the development of the required radar characteristics of surrogate objects. The whole process is divided into two steps. The rst step is to nd the proper methods to measure the radar properties of those three roadside objects. The measurement result of each roadside object will be used as the requirement for making its surrogate. The second step is to create the material for developing the surrogate of each roadside object. In the experimental results demonstrate that all three surrogates satisfy their radar characteristics requirements.