Thesis (Ph. D.)--Ohio State University, 2005. / Title from first page of PDF file. Document formatted into pages; contains xiii, 170 p.; also includes graphics (some col.). Includes bibliographical references (p. 162-170). Available online via OhioLINK's ETD Center

There has been an exponential increase in satellite image data availability. Image data are now collected with different spatial, spectral, and temporal resolutions. Image fusion techniques are used extensively to combine different images having complementary information into one single composite. The fused image has rich information that will improve the performance of image analysis algorithms. Pansharpening is a pixel level fusion technique used to increase the spatial resolution of the multispectral image using spatial information from the high resolution panchromatic image while preserving the spectral information in the multispectral image. Resolution merge, image integration, and multisensor data fusion are some of the equivalent terms used for pansharpening. Pansharpening techniques are applied for enhancing certain features not visible in either of the single data alone, change detection using temporal data sets, improving geometric correction, and enhancing classification. Various pansharpening algorithms are available in the literature, and some have been incorporated in commercial remote sensing software packages such as ERDAS Imagine® and ENVI®. The performance of these algorithms varies both spectrally and spatially. Hence evaluation of the spectral and spatial quality of the pansharpened images using objective quality metrics is necessary. In this thesis, quantitative metrics for evaluating the quality of pansharpened images have been developed. For this study, the Intensity-Hue-Saturation (IHS) based sharpening, Brovey sharpening, Principal Component Analysis (PCA) based sharpening and a Wavelet-based sharpening method is used.

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)

International Telemetering Conference Proceedings / October 18-21, 2004 / Town & Country Resort, San Diego, California / Sensor networks can demand large amounts of bandwidth if the raw sensor data is transferred to a central location. Feature recognition and sensor fusion algorithms can reduce this bandwidth. Unfortunately the designers of the system, having not yet seen the data which will be collected, may not know which algorithms should be used at the time the system is first installed. This paper describes a flexible architecture which allows the deployment of data reduction algorithms throughout the network while the system is in service. The network of sensors approach not only allows for signal processing to be pushed closer to the sensor, but helps accommodate extensions to the system in a very efficient and structured manner.

Vital signs can indicate patient deterioration prior to adverse events such as cardiac arrest, emergency admission to the intensive care unit (ICU), or death. However, many adverse events occur in wards outside the ICU where the level of care and the frequency of patient monitoring are lower than in the ICU. This thesis describes models for detection of deterioration in acutely ill patients in two environments: a step-down unit in which patients recovering from an ICU stay are continuously monitored, and a general ward where patients are intermittently monitored following upper gastrointestinal cancer surgery. Existing data fusion models for classification of vital signs depend on a threshold which defines a “region of normality”. Bradypnoea (low breathing rate) and bradycardia (low heart rate) are relatively rare, and so these two types of abnormalities tend to be misclassified by existing methods. In this thesis, techniques for selecting a threshold are described, such that the classification of vital-sign data is improved. In particular, the proposed approach reduces the misclassification of bradycardia and bradypnoea events, and indicates the type of abnormality associated with the deterioration in a patient’s vital signs. Patients recovering from upper gastrointestinal (GI) surgery have a high risk of emergency admission to the ICU. At present in the UK, most intermediate and general wards outside the ICU depend on intermittent, manual monitoring using track-and-trigger systems. Both manual and automated patient monitoring systems are reported to have high false alert rates. The models described in this thesis take into account the low monitoring frequency in the upper GI ward, such that the false alert rate is reduced. In addition to accuracy, early detection of deterioration is a highly desirable feature in patient monitoring systems. The models proposed in this thesis generate alerts for patients earlier than the early warning systems which are currently in use in hospitals in the UK. The improvements to existing models proposed in this thesis could be applied to continuous and intermittently acquired vital-sign data from other clinical environments.

<p>Flexibility is the keyword when preparing for the uncertainfuture tasks for the civilian and military defence. Supporttools relying on general principles will greatlyfacilitateflexible co-ordination and co-operation between differentcivilian and military organizations, and also between differentcommand levels. Further motivations for general solutionsinclude reduced costs for technical development and training,as well as faster and more informed decisionmaking. Mosttechnical systems that support military activities are howeverdesigned with specific work tasks in mind, and are consequentlyrather inflexible. There are large differences between forinstance fire fighting, disaster relief, calculating missiletrajectories, and navigating large battle-ships. Still, thereought to be much in common in the work of managing thesevarious tasks. We use the term<i>Command and Control</i>(C2) to capture these commonfeatures in management of civilian and military, rescue anddefence operations.</p><p>Consequently, this thesis describes a top-down approach tosupport systems for decision-making in the context of C2, as acomplement to the prevailing bottom-up approaches. DISCCO(Decision Support for Command and Control) is a set ofnetwork-based services including<i>Command Support</i>helping commanders in the human,cooperative and continuous process of evolving, evaluating, andexecuting solutions to their tasks. The command tools providethe means to formulate and visualize tasks, plans, andassessments, but also the means to visualize decisions on thedynamic design of organization. Also included in DISCCO is<i>Decision Support</i>, which, based on AI and simulationtechniques, improve the human process by integrating automaticand semiautomatic generation and evaluation of plans. The toolsprovided by DISCCO interact with a<i>Common Situation Model</i>capturing the recursive structureof the situation, including the status, the dynamicorganization, and the intentions, of own, allied, neutral, andhostile resources. Hence, DISCCOprovides a more comprehensivesituation description than has previously been possible toachieve.</p><p>DISCCO shows generic features since it is designed tosupport a decisionmaking process abstracted from the actualkinds and details of the tasks that are solved. Thus it will beuseful through all phases of the operation, through all commandlevels, and through all the different organizations andactivities that are involved.</p><p><b>Keywords:</b>Command and Control, Management, DecisionSupport, Data Fusion, Information Fusion, Situation Awareness,Network-Based Defence, Ontology.</p>

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Towards having a one size fits all solution to cocaine detection at borders; this thesis proposes a systematic cocaine detection methodology that can use raw data output from a fibre optic sensor to produce a set of unique features whose decisions can be combined to lead to reliable output. This multidisciplinary research makes use of real data sourced from cocaine analyte detecting fibre optic sensor developed by one of the collaborators - City University, London. This research advocates a two-step approach: For the first step, the raw sensor data are collected and stored. Level one fusion i.e. analyses, pre-processing and feature extraction is performed at this stage. In step two, using experimentally pre-determined thresholds, each feature decides on detection of cocaine or otherwise with a corresponding posterior probability. High level sensor fusion is then performed on this output locally to combine these decisions and their probabilities at time intervals. Output from every time interval is stored in the database and used as prior data for the next time interval. The final output is a decision on detection of cocaine. The key contributions of this thesis includes investigating the use of data fusion techniques as a solution for overcoming challenges in the real time detection of cocaine using fibre optic sensor technology together with an innovative user interface design. A generalizable sensor fusion architecture is suggested and implemented using the Bayesian and Dempster-Shafer techniques. The results from implemented experiments show great promise with this architecture especially in overcoming sensor limitations. A 5-fold cross validation system using a 12 13 - 1 Neural Network was used in validating the feature selection process. This validation step yielded 89.5% and 10.5% true positive and false alarm rates with 0.8 correlation coefficient. Using the Bayesian Technique, it is possible to achieve 100% detection whilst the Dempster Shafer technique achieves a 95% detection using the same features as inputs to the DF system.

This thesis deals with the challenge of autonomous navigation of the ExoMars rover. The absence of global positioning systems (GPS) in space, added to the limitations of wheel odometry makes autonomous navigation based on these two techniques - as done in the literature - an inviable solution and necessitates the use of other approaches. That, among other reasons, motivates this work to use solely visual data to solve the robot’s Egomotion problem. The homogeneity of Mars’ terrain makes the robustness of the low level image processing technique a critical requirement. In the first part of the thesis, novel solutions are presented to tackle this specific problem. Detection of robust features against illumination changes and unique matching and association of features is a sought after capability. A solution for robustness of features against illumination variation is proposed combining Harris corner detection together with moment image representation. Whereas the first provides a technique for efficient feature detection, the moment images add the necessary brightness invariance. Moreover, a bucketing strategy is used to guarantee that features are homogeneously distributed within the images. Then, the addition of local feature descriptors guarantees the unique identification of image cues. In the second part, reliable and precise motion estimation for the Mars’s robot is studied. A number of successful approaches are thoroughly analysed. Visual Simultaneous Localisation And Mapping (VSLAM) is investigated, proposing enhancements and integrating it with the robust feature methodology. Then, linear and nonlinear optimisation techniques are explored. Alternative photogrammetry reprojection concepts are tested. Lastly, data fusion techniques are proposed to deal with the integration of multiple stereo view data. Our robust visual scheme allows good feature repeatability. Because of this, dimensionality reduction of the feature data can be used without compromising the overall performance of the proposed solutions for motion estimation. Also, the developed Egomotion techniques have been extensively validated using both simulated and real data collected at ESA-ESTEC facilities. Multiple stereo view solutions for robot motion estimation are introduced, presenting interesting benefits. The obtained results prove the innovative methods presented here to be accurate and reliable approaches capable to solve the Egomotion problem in a Mars environment.

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