Bibliography: leaves 287-294. / xviii, 294 p. : ill. (some col.) ; 30 cm. / Title page, contents and abstract only. The complete thesis in print form is available from the University Library. / This thesis addresses preprocessing issues for radar target classification from high resolution radar imagery. High resolution radar imagery of real aircraft in flight and of a MIRAGE aircraft on a turntable is examined. An algorithm to coherently average high resolution range profile is proposed. / Thesis (Ph.D.)--University of Adelaide, Dept. of Electrical and Electronic Engineering, 1995

Thesis (Ph. D.)--University of Adelaide, Dept. of Electrical and Electronic Engineering, 1995? / Includes bibliographical references (leaves 287-294).

Thesis (M.S.) University of Missouri-Columbia, 2006. / The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research .pdf file viewed on (June 25, 2007) Includes bibliographical references.

This is a report of the second in a projected series of experiments at North Texas State College designed for obtaining information about the microwave scattering properties of various shaped objects.

Thesis (MScEng (Electrical and Electronic Engineering))--University of Stellenbosch, 2009. / ENGLISH ABSTRACT: The origin of chaff dates as far back as WWII, acting as a passive EM countermeasure it was used to confuse enemy radar systems and is still in use today. The purpose of this study is, firstly, to build up a knowledge base for determining chaff parameters and secondly, to calculate the theoretical Radar Cross Section (RCS) of a chaff cloud. Initially dipole resonant properties are investigated relative to dipole physical dimensions. This is extended to the wideband spatial average RCS of a dipole with application to chaff clouds. A model is developed for calculating the theoretical RCS of a cloud typically produced by a single, multiband chaff cartridge. This model is developed on the principles of sparse clouds with negligible coupling; the dipole density for which the model is valid is determined through the statistical simulation of chaff clouds. To determine the effectiveness of chaff clouds, the E-field behaviour through a chaff cloud is investigated numerically. From simulation results a model is developed for estimating the position and drop in E-field strength. It is concluded that though it would be possible to hide a target behind a chaff cloud given ideal circumstances, it is not practical in reality. Given the presented results, recommendations are made for future work. / AFRIKAANSE OPSOMMING: Die oorsprong van kaf dateer so ver terug as WOII. Dit was gebruik as ‘n passiewe EM teenmaatreël teen vyandelike radar stelsels en is steeds vandag in gebruik. Die doel van hierdie studie is eerstens, om ‘n kennisbasis op te bou vir die bepaling van kaf parameters en tweedens, om die teoretiese RDS van kafwolke te bereken. Aanvanklik word die dipool resonante eienskappe ondersoek relatief tot die dipool dimensies. Die studie word uitgebrei tot die wyeband ruimte gemiddelde RDS van ‘n dipool met toepassing op kafwolke. ‘n Model word ontwikkel om die teoretiese RDS te bereken vir ‘n tipiese kafwolk geproduseer deur ‘n enkele, multi-band kafpakkie. Die model is gegrond op die beginsels van lae digte kafwolke met weglaatbare koppeling; die dipool digtheid waarvoor die model geldig is, is bepaal deur statistiese simulasie van kafwolke. Om die effektiwiteit van kafwolke te bepaal, word die E-veld gedrag deur kafwolke numeries ondersoek. Vanaf simulasie resultate word ‘n model ontwikkel om die ligging van, en daling in E-veld sterkte af te skat. Daar word tot die gevolgtrekking gekom dat, alhoewel dit moontlik is om ‘n teiken agter ‘n kafwolk te versteek in ideale omstandighede, dit nie prakties is nie. Na aanleiding van die resultate verkry, is aanbevelings vir verdere werk gedoen.

Rather than emitting pulses, passive radar systems rely on illuminators of opportunity, such as TV and FM radio, to illuminate potential targets. These systems are attractive since they allow receivers to operate without emitting energy, rendering them covert. Until recently, most of the research regarding passive radar has focused on detecting and tracking targets. This dissertation focuses on extending the capabilities of passive radar systems to include automatic target recognition. The target recognition algorithm described in this dissertation uses the radar cross section (RCS) of potential targets, collected over a short period of time, as the key information for target recognition. To make the simulated RCS as accurate as possible, the received signal model accounts for aircraft position and orientation, propagation losses, and antenna gain patterns. An extended Kalman filter (EKF) estimates the target's orientation (and uncertainty in the estimate) from velocity measurements obtained from the passive radar tracker. Coupling the aircraft orientation and state with the known antenna locations permits computation of the incident and observed azimuth and elevation angles. The Fast Illinois Solver Code (FISC) simulates the RCS of potential target classes as a function of these angles. Thus, the approximated incident and observed angles allow the appropriate RCS to be extracted from a database of FISC results. Using this process, the RCS of each aircraft in the target class is simulated as though each is executing the same maneuver as the target detected by the system. Two additional scaling processes are required to transform the RCS into a power profile (magnitude only) simulating the signal in the receiver. First, the RCS is scaled by the Advanced Refractive Effects Prediction System (AREPS) code to account for propagation losses that occur as functions of altitude and range. Then, the Numerical Electromagnetic Code (NEC2) computes the antenna gain pattern, further scaling the RCS. A Rician likelihood model compares the scaled RCS of the illuminated aircraft with those of the potential targets. To improve the robustness of the result, the algorithm jointly optimizes over feasible orientation profiles and target types via dynamic programming.

No description available.

Parametric modeling has many applications. These applications include data estimation and interpolation, modern spectral estimation, and data encoding. This research applies parametric modeling to radar cross section data in an attempt to encode it as well as preserve its spectrum. Traditionally, radar data has been processed through Fourier spectral estimation techniques. These methods not only require large amounts of data, for good spectral estimates, but assume the unobserved data values are zero which leads to spectral smearing. Modern spectral estimation methods alleviate these problems by basing the spectral estimate on a parametric model fit to the data set. The spectral estimate is then derived from the parameters of the model. For models which give a good fit to the data, a good spectral estimate can be made. The most common parametric models are the autoregressive moving-average (ARMA), the moving-average (MA) and the autoregressive (AR) model. These models represent filters, which when excited by a white Gaussian noise sequence give some output sequence. If the parameters of the models and the noise sequence are selected properly, a desired output data sequence can be modeled. The variance of the white noise is often small compared to the variance of the data sequence. This means that the model parameters plus the noise can be stored with fewer bits than the original data sequence while maintaining the same amount of accuracy in the data. The model parameters and noise sequence can be used to reproduce the original data sequence. Further, if only the spectrum of the data is of interest, only the noise variance plus the parameters need to be stored. This could lead to an even greater amount of data reduction. Most high resolution radar data applications require only that the spectrum of the data be preserved which makes modern spectral estimation appealing. This research project applies parametric modeling and modern spectral estimation to high resolution radar data as a means of encoding it. Several different parametric modeling techniques are evaluated to see which would be most useful in radar data encoding. The Burg AR parametric model was chosen due to its computational efficiency and its good spectral estimates. The Burg method applied to two radar range profile data sets gave a reduction in data storage by a factor of four. Further encoding was achieved by fitting the Burg AR parameters to a set of basis functions. This produced an additional data reduction by a factor of 36, for a total compression factor of 144. The latter led to some distortion of the high resolution range profiles, yet targets were still sufficiently characterized. / Master of Science

Because of the great difficulty of obtaining exact numerical values of cross-section, and because of the inherent uncertainties in interpreting and evaluating the approximate methods, accurate experimental cross-section data would be extremely useful to the radar engineer. It was with this purpose in mind that the present long-range research program in microwave scattering was undertaken. Of immediate interest were the scattering properties of the prolate spheroid, the ogive (formed by rotating the minor segment of a circle around the chord), and, for comparison, the long cylinder.

Synthetic aperture radar (SAR) is one of the most versatile tools ever invented for imaging. Due to its better Rayleigh resolution, SAR imaging provides the highest quality radar imagery. These images are used for many applications including but not limited to terrestrial mapping, disaster reconnaissance, medical imaging and military applications. Imaging techniques or geometries which can improve the resolution of the reconstructed imagery is always desired in the SAR imaging. In this dissertation both the linear and nonlinear frequency modulated chirp signals are discussed. The most widely used frequency modulated chirp signal, linear frequency modulated chirp signal, and some of its properties such as spectrum, point spread function and matched filter are summarized. A new nonlinear frequency modulated chirp signal which can be used to improve the image resolution is introduced. In order to validate the offered chirp signal, spotlight SAR imaging geometry together with 2D polar and Stolt format reconstruction algorithms are considered. The synthetic examples are generated using both chirps both with polar and Stolt format processing. Additionally a new change detection method which depends on the idea of generating two different final change maps of the initial and final images in a sequence is offered. The specific algorithms utilized for testing this method are the widely used correlation coefficient change statistic and the intensity ratio change statistic algorithms. This method together with the algorithms mentioned is first applied to synthetic data generated by Stolt format processing. It is shown that the method works on synthetic data. The method together with the algorithms mentioned is also applied to two case studies dfreal disasters, one is 2010 Gulf of Mexico oil spill and the second is 2008 China Sichuan earthquake. It is shown that two final change map method can reduce the false identifications of the changes. Also it is shown that intensity ratio change statistics is a better tool for identifying the changes due to oil contamination. The data used in this study is acquired by Japanese Aerospace Agency's Advanced Land Observing Satellite (ALOS) through Alaska SAR Facility (ASF), at the University of Alaska, Fairbanks.