Tracking systems are already encountered in everyday life in numerous applications, but many algorithms from the existing literature rely on assumptions that do not always hold in realistic scenarios, or can only be applied in niche circumstances. Therefor this thesis is motivated to develop new approaches that relax assumptions and restrictions, improve tracking performance, and are applicable in a broad range of scenarios. In the area of terrain-aided tracking this an algorithm is proposed to track targets using a Gaussian mixture measurement distribution to better represent multimodal distributions that can arise due to terrain conditions. This allowed effective use in a wider range of terrain conditions than existing approaches, which assume a unimodal Gaussian measurement distribution. Next, the problem of estimating and compensating for sensor biases is considered in the context of terrain-aided tracking. Existing approaches to bias estimation cannot be easily reconciled with the nonlinear converted measurement model applied in terrain-aided tracking. To address this, a novel efficient bias estimation algorithm is proposed that can be applied to a wide range of measurement models and operational scenarios, allowing for effective bias estimation and measurement compensation to be performed in situations that cannot be handled by existing algorithms. Finally, to address scenarios where converted measurement tracking is not possible or desired, the problem of sensor motion compensation when tracking in pixel coordinates is considered. Existing approaches compensate for sensor motion by transforming state estimates between frames, but are only able to achieve partial transformation of the state estimate and its covariance matrix. This thesis proposes a novel algorithm used to transform the full state estimate and its covariance matrix, improving tracking performance when tracking with a low frame rate and when tracking targets moving with a nearly coordinated turn motion model. Each of the proposed algorithms are evaluated in several simulated scenarios and compared against existing approaches and baselines to demonstrate their efficacy. / Thesis / Doctor of Philosophy (PhD)

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<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>

eXfiltration Advanced Persistent Threats (XAPTs) increasingly account for incidents concerned with critical information exfiltration from High Valued Targets (HVT's) by terrorists, cyber criminals or enemy states. Existing Cyber Defence frameworks and data fusion models do not adequately address (i) the multi-stage nature of XAPTs and (ii) the uncertainty and conflicting information associated with XAPTs. A new data fusion theory, called the Markov Multi-phase Transferable Belief Model (MM-TBM) is developed, for tracking and predicting XAPTs. MM-TBM expands the attack kill-chain model to attack trees and introduces a novel approach for combining various sources of cyber evidence, which takes into account the multi-phased nature of XAPTs and the characteristics of the cyberspace. As a data fusion theory, MM-TBM constitutes a novel approach for performing hypothesis assessment and evidence combination across phases, by means of a new combination rule, called the Multi-phase Combination Rule with conflict Reset (MCR2). This is the first combination rule in the field of data fusion that formalises a new method for combining evidence from multiple, causally connected hypotheses spaces and eliminating the bias from preceding phases of the kill-chain. Moreover, this is the first time a data fusion theory utilises the conflict mass m(Ø) for identifying paradoxes. In addition, a diagnostic formula for managing missing pieces of evidence within attack trees is presented. MM-TBM is designed, developed and evaluated using a Design Science Research approach within two iterations. Evaluation is conducted in a relevant computer network environment using scenario-based testing. The experimental design has been reviewed and approved by Cyber Security Subject Matter Experts from MoD’s Defence Science Technology Laboratory and Airbus Group. The experimental results validate the novel capabilities introduced by the new MM-TBM theory to Cyber Defence in the presence of information clutter, conflict and congestion. Furthermore, the results underpin the importance of selecting an optimal sampling policy to effectively track and predict XAPTs. This PhD bridges the gaps in the body of knowledge concerned with multi-phase fusion under uncertainty and Cyber SA against XAPTs. MM-TBM is a novel mathematical fusion theory for managing applications that existing fusion models do not address. This research has demonstrated MM-TBM enables the successful Tracking and Prediction of XAPTs to deliver an enhanced Cyber SA capability.

The aim of the project was to identify biologically relevant novel gene clusters by using combined genomic data instead of using only gene expression data in isolation. The clustering algorithm based on self-organizing maps (Kasturi et al., 2005) was extended and implemented in order to use gene location data together with the gene expression and the motif occurrence data for gene clustering. A distance function was defined to be used with gene location data. The algorithm was also extended in order to use vector angle distance for gene expression data. Arabidopsis thaliana is chosen as a data source to evaluate the developed algorithm. A test data set was created by using 100 Arabidopsis genes that have gene expression data with seven different time points during cold stress condition, motif occurrence data which indicates the occurrence frequency of 614 different motifs and the chromosomal location data of each gene. Gene Ontology (http://www.geneontology.org) and TAIR (http://arabidopsis.org) databases were used to find the molecular function and biological process information of each gene in order to examine the biological accuracy of newly discovered clusters after using combined genomic data. The biological evaluation of the results showed that using combined genomic data to cluster genes resulted in new biologically relevant clusters.

In this dissertation, the issues of the increased awareness of energy use are considered. Energy technologies are continuously improved by energy retailers and academic researchers. The Smart Grid are soon customary as part of the energy domain. But in order to improve energy efficiency the change must come from the consumers. Consumers should be active decision makers in the Smart Grid domain and therefor a Recommender system suits the Smart Grid and enables customers. Customers will not use energy in the way energy retailers, and politicians advocates instead they will do what fits them. By investigating how a Recommender can be built in the Smart Grid we focus on parameters and information that supports the costumers and enables positive change. An investigation of what customers perceive as relevant is pursued as well as how relevancy can adjust the system. A conceptual model of how to build a Recommender is rendered through a literature review, a group interview and a questionnaire.

In the post September 11 era, the demand for security has increased in virtually all parts of the society. The need for increased security originates from the emergence of new threats which differ from the traditional ones in such a way that they cannot be easily defined and are sometimes unknown or hidden in the “noise” of daily life. When the threats are known and definable, methods based on situation recognition can be used find them. However, when the threats are hard or impossible to define, other approaches must be used. One such approach is data-driven anomaly detection, where a model of normalcy is built and used to find anomalies, that is, things that do not fit the normal model. Anomaly detection has been identified as one of many enabling technologies for increasing security in the society. In this thesis, the problem of how to detect anomalies in the surveillance domain is studied. This is done by a characterisation of the surveillance domain and a literature review that identifies a number of weaknesses in previous anomaly detection methods used in the surveillance domain. Examples of identified weaknesses include: the handling of contextual information, the inclusion of expert knowledge and the handling of joint attributes. Based on the findings from this study, a new anomaly detection method is proposed. The proposed method is evaluated with respect to detection performance and computational cost on a number datasets, recorded from real-world sensors, in different application areas of the surveillance domain. Additionally, the method is also compared to two other commonly used anomaly detection methods. Finally, the method is evaluated on a dataset with anomalies developed together with maritime subject matter experts. The conclusion of the thesis is that the proposed method has a number of strengths compared to previous methods and is suitable foruse in operative maritime command and control systems. / Christoffer Brax forskar också vid högskolan i Skövde, Informatics Research Centre / Christoffer Brax also does research at the University of Skövde, Informatics Research Centre

<p>The aim of the project was to identify biologically relevant novel gene clusters by using combined genomic data instead of using only gene expression data in isolation. The clustering algorithm based on self-organizing maps (Kasturi et al., 2005) was extended and implemented in order to use gene location data together with the gene expression and the motif occurrence data for gene clustering. A distance function was defined to be used with gene location data. The algorithm was also extended in order to use vector angle distance for gene expression data. <em>Arabidopsis thaliana</em> is chosen as a data source to evaluate the developed algorithm. A test data set was created by using 100 Arabidopsis genes that have gene expression data with seven different time points during cold stress condition, motif occurrence data which indicates the occurrence frequency of 614 different motifs and the chromosomal location data of each gene. Gene Ontology (http://www.geneontology.org) and TAIR (http://arabidopsis.org) databases were used to find the <em>molecular function</em> and <em>biological process</em> information of each gene in order to examine the biological accuracy of newly discovered clusters after using combined genomic data. The biological evaluation of the results showed that using combined genomic data to cluster genes resulted in new biologically relevant clusters.</p>