Thesis (M.S.)--University of Florida, 2005. / Title from title page of source document. Document formatted into pages; contains 103 pages. Includes vita. Includes bibliographical references.

Thesis (M.S.)--University of Texas at El Paso, 2007. / Title from title screen. Vita. CD-ROM. Includes bibliographical references. Also available online.

Thesis (MScIng)--University of Stellenbosch, 2006. / Bibliography. Also available via the Internet.

Approved for public release, distribution is unlimited / Radar polarimetry is a recent development seeing active research only in the last few decades. The phenomenon that optimal (maximal power) reflected fields exist in both the co-polarized and cross polarized channels of the receiving radar antenna was first introduced by Kennaugh and Huynen. Current research efforts focus on target scattering matrices and relating them to physical attributes of the target. This thesis provides a comprehensive survey of the polarimetry theories that have been put forth by various researchers to characterize, manipulate and optimize target radar returns via polarization states. One such theory is the Target Decomposition (TD) theorem that seeks to decompose the target returns into individual scattering mechanisms. The topic of optimization of polarization states of the incident field for maximizing power return is also examined. Two models are implemented in Matlab to verify and demonstrate these polarimetry theories. The first model uses TD theorems to simulate foliage clutter and study its effect on the polarization of the incident electric field. A (simulated) static dihedral target is introduced and its effect on wave polarization is also simulated. The second model studies optimization of polarization states. Both models are able to produce the expected results for known canonical targets. / Civilian, Republic of Singapore

Thesis (M.A.Sc.)--Naval Postgraduate School, 2001 / Includes bibliographical references (p. 163-166). Also available in electronic format via the Defense Technical Information Center website.

Thesis (M.A.Sc.)--Naval Postgraduate School, 2000. / Includes bibliographical references (p. 157-159). Also available in electronic format via the Defense Technical Information Center website.

Thesis (MScEng)--Stellenbosch University, 2014. / ENGLISH ABSTRACT: This work entails the development and performance analysis of a human gait detection system based on radar micro-Doppler signals. The system consists of a tracking functionality and a target classifier. Target micro-Doppler signatures are extracted with Short-Time Fourier Transform (STFT) based spectrogram providing a high-resolution signatures with the radar that is used. A feature extraction mechanism is developed to extract six features from the signature and an artificial neural network (A-NN) based classifier is designed to carry out the classification process. The system is tested on real X-band radar data of human subjects performing six activities. Those activities are walking and speed walking, walking with hands in pockets, marching, running, walking with a weapon, and walking with arms swaying. The multiclass classifier was designed to discriminate between those activities. High classification accuracy of 96% is demonstrated. / AFRIKAANSE OPSOMMING: Hierdie werk behels die ontwikkeling, en analise van werksverrigting, van ’n menslike stapdetekor gebaseer op radar-mikrodoppleranalise. Die stelsel bestaan uit ’n teikenvolger en -klassifiseerder. Die mikrodoppler-kenmerke van ’n teiken word met behulp van die korttyd-Fourier-transform onttrek, en verskaf hoe-resolusie-kenmerke met die radar wat vir die implementering gebruik word. ’n Kenmerkontrekkingstelsel is ontwikkel om ses kenmerke vanuit die spektrogram te onttrek, en ’n kunsmatige neurale netwerk word as klassifiseerder gebruik. Die stelsel is met ’n X-band radar op werklike menslike beweging getoets, terwyl vrywilligers ses aktiwiteite uitgevoer het: loop, loop (hand in die sakke), marsjeer, hardloop, loop met ’n wapen, loop met arms wat swaai. Die multiklas-klassifiseerder is ontwerp om tussen hierdie aktiwiteite te onderskei. ’n Hoe klassifiseringsakkuraatheid van 96% word gedemonstreer.

Thesis (MSc (Mathematical Sciences. Applied Mathematics))--University of Stellenbosch, 2006. / In this work we present a novel method to do measurement correlation between target observations made by two ormore radar systems. Some of the most common radar sensors available are those measuring only range (distance to the target) and bearing (azimuth angle). We use these measurements to determine the correlation between two di¤erent sensors observing the same target. As a by-product of the correlation algorithm, we nd a way to estimate the target height for a target observed by at least two radar sensors. The correlation method is expounded upon, where we discuss measurement correlation for moving targets. Targets are tracked using a Kalman Filter, and correlation is done between new observations and existing target tracks. Finally, the correlation algorithm is implemented in an interactive 3D computer simulation. Results obtained indicate a high success rate, with false correlations only obtained where sensor accuracy is the limiting factor.

Thesis (MScEng (Mathematical Sciences. Applied Mathematics))--University of Stellenbosch, 2005. / Tracking manoeuvring military airborne targets with radar is problematic due to the low scan rates and the high levels of measurement noise. Surveillance systems using multiple radars have the benefit of an increased rate of observation and noise reduction but also have the problem of correlating observations from multiple sensors. Mehtods are discussed to correlate single observations from multiple radar sensors as well as assigning observations to existing tracks. Filtering methods to reduce measurement noise of the target tracks and methods to extrapolate the predicted position of targets are also explored.

La reconnaissance automatique de cibles non coopératives est d’une grande importance dans divers domaines. C’est le cas pour les applications en environnement incertain aérien et maritime. Il s’avère donc nécessaire d’introduire des méthodes originales pour le traitement et l’identification des cibles radar. C’est dans ce contexte que s’inscrit notre travail. La méthodologie proposée est fondée sur le processus d’extraction de connaissance à partir de données (ECD) pour l’élaboration d’une chaine complète de reconnaissance à partir des images radar en essayant d’optimiser chaque étape de cette chaine de traitement. Les expérimentations réalisées pour constituer une base de données d’images ISAR ont été effectuées dans la chambre anéchoïque de l’ENSTA de Bretagne. Ce dispositif de mesures utilisé a l’avantage de disposer d’une maîtrise de la qualité des données représentants les entrées dans le processus de reconnaissance (ECD). Nous avons ainsi étudié les étapes composites de ce processus de l’acquisition jusqu’à l’interprétation et l’évaluation de résultats de reconnaissance. En particulier, nous nous sommes concentrés sur l’étape centrale dédiée à la fouille de données considérée comme le cœur du processus développé. Cette étape est composée de deux phases principales : une porte sur la classification et l’autre sur la fusion des résultats des classifieurs, cette dernière est nommée fusion décisionnelle. Dans ce cadre, nous avons montré que cette dernière phase joue un rôle important dans l’amélioration des résultats pour la prise de décision tout en prenant en compte les imperfections liées aux données radar, notamment l’incertitude et l’imprécision. Les résultats obtenus en utilisant d’une part les différentes techniques de classification (kppv, SVM et PMC), et d’autre part celles de de fusion décisionnelle (Bayes, vote, théorie de croyance, fusion floue) font l’objet d’une étude analytique et comparative en termes de performances. / The automatic recognition of non-cooperative targets is very important in various fields. This is the case for applications in aviation and maritime uncertain environment. Therefore, it’s necessary to introduce innovative methods for radar targets treatment and identification.The proposed methodology is based on the Knowledge Discovery from Data process (KDD) for a complete chain development of radar images recognition by trying to optimize every step of the processing chain.The experimental system used is based on an ISAR image acquisition system in the anechoic chamber of ENSTA Bretagne. This system has allowed controlling the quality of the entries in the recognition process (KDD). We studied the stages of the composite system from acquisition to interpretation and evaluation of results. We focused on the center stage; data mining considered as the heart of the system. This step is composed of two main phases: classification and the results of classifiers combination called decisional fusion. We have shown that this last phase improves results for decision making by taking into account the imperfections related to radar data, including uncertainty and imprecision.The results across different classification techniques as a first step (kNN, SVM and MCP) and decision fusion in a second time (Bayes, majority vote, belief theory, fuzzy fusion) are subject of an analytical and comparative study in terms of performance.