Universiẗat, Diss., 1999--Stuttgart.

No description available.

No description available.

No description available.

No description available.

Data fusion is the methodology of efficiently combining the relevant information from different sources. The goal is to achieve estimates and inferences with better confidence than those achievable by relying on a single source. Initial data fusion applications were predominantly in defense: target tracking, threat assessment and land mine detection. Nowadays, data fusion is applied to robotics (e.g., environment identification for navigation), medicine (e.g., medical diagnosis), geoscience (e.g., data integration from different sources) and industrial engineering (e.g., fault detection). This thesis focuses on data fusion for distributed multisensor tracking systems. In these systems, each sensor can provide the information as measurements or local estimates, i.e., tracks. The purpose of this thesis is to advance the research in the fusion of local estimates for multisensor multitarget tracking systems, namely, track fusion. This study also proposes new methods for track-to-track association, which is an implicit subproblem of track fusion. The first contribution is for the case where local sensors perform tracking using particle filters (Monte Carlo based methods). A method of associating tracks estimated through labeled particle clouds is developed and demonstrated with subsequent fusion. The cloud-to-cloud association cost is devised together with computation methods for the general and specialized cases. The cost introduced is proved to converge (with increasing clouds cardinality) toward the corresponding distance between the underlying distributions. In order to simulate the method introduced, a particle filter labeled at particle level was developed, based on the Probability Hypothesis Density (PHD) particle filter. The second contribution is for the case where local sensors produce tracks using Kalman filter-type estimators, in the form of track state estimate and track state covariance matrix. For this case the association and fusion is improved in both terms of accuracy and identity, by introducing at each fusion time the prior information (both estimate and identity) from the previous fusion time. The third contribution is for the case where local sensors produce track estimates under the form of MHT, therefore where each local sensor produces several hypotheses of estimates. A method to use the information from other sensors in propagating each sensor's internal hypotheses over time is developed. A practical fusion method for real world local tracking sensors, i.e., asynchronous and with incomplete information available, is also developed in this thesis. / Thesis / Doctor of Philosophy (PhD)

Approved for public release; distribution is unlimited / In the boost phase interception of ballistic missiles, determining the exact position of a ballistic missile has a significant importance. Several sensors are used to detect and track the missile. These sensors differ from each other in many different aspects. The outputs of radars give range, elevation and azimuth information of the target while space based infrared sensors give elevation and azimuth information. These outputs have to be combined (fused) achieve better position information for the missile. The architecture that is used in this thesis is decision level fusion architecture. This thesis examines four algorithms to fuse the results of radar sensors and space based infrared sensors. An averaging technique, a weighted averaging technique, a Kalman filtering approach and a Bayesian technique are compared. The ballistic missile boost phase segment and the sensors are modeled in MATLAB. The missile vector and dynamics are based upon Newton's laws and the simulation uses an earth-centered coordinate system. The Bayesian algorithm has the best performance resulting in a rms missile position error of less than 20 m. / 1st Lieutenant, Turkish Air Force

As the United States Navy strives to attain a myriad of situational awareness systems that provide the functionality and interoperability required for future missions, the fundamental idea of open architecture is beginning to promulgate throughout the Department. In order to make rational, informed decisions concerning the processes and systems that will be integrated to provide this situational awareness, an analytical method must be used to identify process deficiencies and produce quantifiable measurement indicators. This thesis will apply the Knowledge Value Added methodology to the current processes involved in track management aboard the AEGIS and Ship Self Defense System (SSDS) platforms. Additional analysis will be conducted based on notional changes that could occur were the systems designed using an open architecture approach. A valuation based on knowledge assets will be presented in order to.

Active vision systems are about mobile platform equipped with one or more than one cameras. They perceive what happens in their surroundings from the image streams the cameras grab. Such systems have a few fundamental tasks to tackle---they need to determine from time to time what their motion in space is, and should they have multiple cameras, they need to know how the cameras are relatively positioned so that visual information collected by the respective cameras can be related. In the simplest form, the tasks are about finding the motion of a camera, and finding the relative geometry of every two cameras, from the image streams the cameras collect. / On determining the ego-motion of a camera, there have been many previous works as well. However, again, most of the works require to track distinct features in the image stream or to infer the full optical flow field from the normal flow field. Different from the traditional works, utilizing no motion correspondence nor the epipolar geometry, a new method is developed that operates again on the normal flow data directly. The method has a number of features. It can employ the use of every normal flow data, thus requiring less texture from the image scene. A novel formulation of what the normal flow direction at an image position has to offer on the camera motion is given, and this formulation allows a locus of the possible camera motion be outlined from every data point. With enough data points or normal flows over the image domain, a simple voting scheme would allow the various loci intersect and pinpoint the camera motion. / On determining the relative geometry of two cameras, there already exist a number of calibration techniques in the literature. They are based on the presence of either some specific calibration objects in the imaged scene, or a portion of the scene that is observable by both cameras. However, in active vision, because of the "active" nature of the cameras, it could happen that a camera pair do not share much or anything in common in their visual fields. In the first part of this thesis, we propose a new solution method to the problem. The method demands image data under a rigid motion of the camera pair, but unlike the existing motion correspondence-based calibration methods it does not estimate the optical flows or motion correspondences explicitly. Instead it estimates the inter-camera geometry from the monocular normal flows. Moreover, we propose a strategy on selecting optimal groups of normal flow vectors to improve the accuracy and efficiency of the estimation. / The relative motion between a camera and the imaged environment generally induces a flow field in the image stream captured by the camera. The flow field, which is about motion correspondences of the various image positions over the image frames, is referred to as the optical flows in the literature. If the optical flow field of every camera can be made available, the motion of a camera can be readily determined, and so can the relative geometry of two cameras. However, due to the well-known aperture problem, directly observable at any image position is generally not the full optical flow, but only the component of it that is normal to the iso-brightness contour of the intensity profile at the position. The component is widely referred to as the normal flow. It is not impossible to infer the full flow field from the normal flow field, but then it requires some specific assumptions about the imaged scene, like it is smooth almost everywhere etc. / This thesis aims at exploring how the above two fundamental tasks can be tackled by operating on the normal flow field directly. The objective is, without the full flow inferred explicitly in the process, and in turn no specific assumption made about the imaged scene, the developed methods can be applicable to a wider set of scenes. The thesis consists of two parts. The first part is about how the inter-camera geometry of two cameras can be determined from the two monocular normal flow fields. The second part is about how a camera's ego-motion can be determined by examining only the normal flows the camera observes. / We have tested the methods on both synthetic image data and real image sequences. Experimental results show that the developed methods are effective in determining inter-camera geometry and camera motion from normal flow fields. / Yuan, Ding. / Adviser: Ronald Chung. / Source: Dissertation Abstracts International, Volume: 70-09, Section: B, page: . / Thesis submitted in: October 2008. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2009. / Includes bibliographical references (leaves 121-131). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. [Ann Arbor, MI] : ProQuest Information and Learning, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstracts in English and Chinese. / School code: 1307.

Les avancées technologiques de ces dernières années, combinées au succès avéré et toujours croissant des communications sans fil, ont tout naturellement donné naissance à un nouveau type de réseaux sans fil, communément appelés Body Area networks. A terme, ces réseaux corporels sans fil doivent permettre à un ensemble de senseurs bio-médicaux répartis sur le corps humain de communiquer, soit pour échanger des informations en vue d'un traitement en temps réel du patient, soit pour enregistrer des données physiologiques en vue d'une analyse ultérieure. L’objectif de cette Thèse vise la réduction de la consommation énergétique au niveau des senseurs de sorte à leur garantir une autonomie de quelques mois, voire de quelques années. En réponse à cette contrainte énergétique, une association innovante de deux technologies émergentes est proposée, à savoir une combinaison des transmissions à ultra-large bande aux systèmes à multiples antennes. Une nouvelle architecture pour les réseaux corporels sans fil est donc envisagée pour laquelle les performances doivent être évaluées. Notre principale contribution à cet objectif consiste en la proposition d'une modélisation spatio-temporelle complète du canal de transmission dans le cadre de senseurs répartis autour du corps. Cette modélisation fait appel à la définition de nouveaux modèles, l'élaboration d'outils spécifiques d'extraction de paramètres et une compréhension fine des mécanismes de propagation liés à la proximité du corps humain. Ce manuscrit présente les résultats majeurs de nos recherches en cette matière.