A model-based reasoning architecture for system-level fault diagnosis

This dissertation presents a model-based reasoning architecture with a two fold purpose: to detect and classify component faults from observable system behavior, and to generate fault propagation models so as to make a more accurate estimation of current operational risks. It incorporates a novel approach to system level diagnostics by addressing the need to reason about low-level inaccessible components from observable high-level system behavior. In the field of complex system maintenance it can be invaluable as an aid to human operators. The first step is the compilation of the database of functional descriptions and associated fault-specific features for each of the system components. The system is then analyzed to extract structural information, which, in addition to the functional database, is used to create the structural and functional models. A fault-symptom matrix is constructed from the functional model and the features database. The fault threshold levels for these symptoms are founded on the nominal baseline data. Based on the fault-symptom matrix and these thresholds, a diagnostic decision tree is formulated in order to intelligently query about the system health. For each faulty candidate, a fault propagation tree is generated from the structural model. Finally, the overall system health status report includes both the faulty components and the associated at risk components, as predicted by the fault propagation model.Ph.D.Committee Chair: Vachtsevanos, George; Committee Member: Liang, Steven; Committee Member: Michaels, Thomas; Committee Member: Vela, Patricio; Committee Member: Wardi, Yora

Autonomous Drone Landings on an Unmanned Marine Vehicle using Deep Reinforcement Learning

This thesis describes with the integration of an Unmanned Surface Vehicle (USV) and an Unmanned Aerial Vehicle (UAV, also commonly known as drone) in a single Multi-Agent System (MAS). In marine robotics, the advantage offered by a MAS consists of exploiting the key features of a single robot to compensate for the shortcomings in the other. In this way, a USV can serve as the landing platform to alleviate the need for a UAV to be airborne for long periods time, whilst the latter can increase the overall environmental awareness thanks to the possibility to cover large portions of the prevailing environment with a camera (or more than one) mounted on it. There are numerous potential applications in which this system can be used, such as deployment in search and rescue missions, water and coastal monitoring, and reconnaissance and force protection, to name but a few. The theory developed is of a general nature. The landing manoeuvre has been accomplished mainly identifying, through artificial vision techniques, a fiducial marker placed on a flat surface serving as a landing platform. The raison d'etre for the thesis was to propose a new solution for autonomous landing that relies solely on onboard sensors and with minimum or no communications between the vehicles. To this end, initial work solved the problem while using only data from the cameras mounted on the in-flight drone. In the situation in which the tracking of the marker is interrupted, the current position of the USV is estimated and integrated into the control commands. The limitations of classic control theory used in this approached suggested the need for a new solution that empowered the flexibility of intelligent methods, such as fuzzy logic or artificial neural networks. The recent achievements obtained by deep reinforcement learning (DRL) techniques in end-to-end control in playing the Atari video-games suite represented a fascinating while challenging new way to see and address the landing problem. Therefore, novel architectures were designed for approximating the action-value function of a Q-learning algorithm and used to map raw input observation to high-level navigation actions. In this way, the UAV learnt how to land from high latitude without any human supervision, using only low-resolution grey-scale images and with a level of accuracy and robustness. Both the approaches have been implemented on a simulated test-bed based on Gazebo simulator and the model of the Parrot AR-Drone. The solution based on DRL was further verified experimentally using the Parrot Bebop 2 in a series of trials. The outcomes demonstrate that both these innovative methods are both feasible and practicable, not only in an outdoor marine scenario but also in indoor ones as well

A technique for determining viable military logistics support alternatives

A look at today's US military will see them operating much beyond the scope of protecting and defending the United States. These operations now consist of, but are not limited to humanitarian aid, disaster relief, and conflict resolution. This broad spectrum of operational environments has necessitated a transformation of the individual military services into a hybrid force that can leverage the inherent and emerging capabilities from the strengths of those under the umbrella of the Department of Defense (DOD), this concept has been coined Joint Operations. Supporting Joint Operations requires a new approach to determining a viable military logistics support system. The logistics architecture for these operations has to accommodate scale, time, varied mission objectives, and imperfect information. Compounding the problem is the human in the loop (HITL) decision maker (DM) who is a necessary component for quickly assessing and planning logistics support activities. Past outcomes are not necessarily good indicators of future results, but they can provide a reasonable starting point for planning and prediction of specific needs for future requirements. Adequately forecasting the necessary logistical support structure and commodities needed for any resource intensive environment has progressed well beyond stable demand assumptions to one in which dynamic and nonlinear environments can be captured with some degree of fidelity and accuracy. While these advances are important, a holistic approach that allows exploration of the operational environment or design space does not exist to guide the military logistician in a methodical way to support military forecasting activities. To bridge this capability gap, a method called A Technique for Logistics Architecture Selection (ATLAS) has been developed. This thesis describes and applies the ATLAS method to a notional military scenario that involves the Navy concept of Seabasing and the Marine Corps concept of Distributed Operations applied to a platoon sized element. This work uses modeling and simulation to incorporate expert opinion and knowledge of military operations, dynamic reasoning methods, and certainty analysis to create a decisions support system (DSS) that can be used to provide the DM an enhanced view of the logistics environment and variables that impact specific measures of effectiveness.Ph.D.Committee Chair: Mavris, Dimitri; Committee Member: Fahringer, Philip; Committee Member: Nixon, Janel; Committee Member: Schrage, Daniel; Committee Member: Soban, Danielle; Committee Member: Vachtsevanos, Georg

기술 포트폴리오 평가를 포함한 미래항공 모빌리티의 확률론적 설계 프로세스

학위논문(박사) -- 서울대학교대학원 : 공과대학 기계항공공학부, 2023. 2. 이관중.Within the last several decades, breakthroughs in multiple disciplines of aircraft technology and design have paved the way to the advent of a novel aircraft system that is collectively referred to as advanced air mobility these days. Specifically, the increasing maturity of electrified propulsion technologies is one of the most powerful drivers for various configurations for advanced air mobility and its possible operation in urban areas. The novelty of advanced air mobility makes it difficult to use historical data accumulated during over half of a century in the earlier design phase where numerous iterative processes are carried out to derive design requirements and initial sizing layout and information as a starting point of the design. Therefore, a physics-based design approach is necessary for the initial sizing of advanced air mobility and the conceptual design has become more significant. In efforts to derive a more reliable and credible design for advanced air mobility, the improvements in two primary tasks in the conceptual design phase were achieved with the use of the statistical and probabilistic methodology in this study. The first task is technology assessment to list up available technologies and decide which technology portfolio could bring the success of aircraft development with the maximum effectiveness and the minimum cost increasement. Presented is an uncertainty-based technology portfolio assessment framework based on mathematical formulations that are more realistic and practical in addition to taking into account the interaction between technologies and uncertainties associated with the impact of technologies and the surrogate model itself. This method possibly enables elevating the level of knowledge in the conceptual design phase, which eventually leads to a reduction of the number of iterative design feedbacks and committed cost for the life cycle of the advanced air mobility. The second task is sizing to obtain overall dimension and weight distribution for the further design phases. Not only was presented a deterministic sizing framework for advanced air mobility firstly, but uncertainties from physical geometric parameters and simplified mathematical analysis modules were also identified and imposed into the sizing framework with Monte Carlo simulation. The expansion to uncertainty-incorporated sizing allows securing a proper buffer or margin in sizing result, and allows understanding the system response to the uncertainties in the earlier design phase, which makes decision-makers prepare for the next design phase. Both improved frameworks were demonstrated on a hypothetical advanced air mobility of vertical take-off and landing configuration with full electric propulsion system, respectively. The demonstrations showed the validity of the presented frameworks providing ways for utilization and interpretation of their application consequences. Both uncertainty-based frameworks for technology portfolio assessment and sizing of advanced air mobility are platform-agnostic frameworks that are applicable to various aircraft development programs. Hence, the base philosophy of the frameworks can be shared broadly.지난 수십 년 동안, 항공기의 여러 학제에서 성취한 획기적인 기술발전으로 오늘날 미래항공 모빌리티로 통칭되는 새로운 항공 시스템이 출현하였다. 특히, 전기동력 추진기술의 성숙도 향상은 미래항공 모빌리티의 다양한 형상과 도심 지역에서의 운용을 가능하게 만든 가장 강력한 요인 중 하나이다. 미래항공 모빌리티의 새로운 특성은 항공기 설계의 시작점인 초기 설계단계에서 지난 반세기 동안 축적된 과거 데이터를 사용하는데 어려움을 유발한다. 따라서 미래항공 모빌리티의 초기 사이징은 물리이론 기반의 설계 접근법을 요구하고, 이와 동시에 개념설계단계의 중요성이 이전에 비해 더욱 상승하였다. 신뢰성 있는 미래항공 모빌리티 설계결과를 도출하기 위한 노력의 일환으로, 본 연구에서는 통계적, 확률론적 방법론을 접목시켜 개념설계단계에서 다뤄지는 중요한 두 가지 주요 업무를 개선하였다. 첫 번째는 효용 최대화와 비용 최소화로 항공기 개발을 성공으로 이끌 기술 포트폴리오를 결정하는 "기술평가" 업무이다. 본 연구에서 제시된 불확실성 기반 기술 포트폴리오 평가 프레임워크는 기술 간의 상호 작용과 기술 효과 예측에서 개선된 수학모델을 수립하였다. 또한 근사모델에 존재하는 불확실성을 고려하여 보다 현실적이고 실용적인 결과를 도출할 수 있다. 이 방법론을 통해 개념설계단계에서 의사결정에 필요한 정보 및 지식 수준을 높일 수 있으며, 이는 결과적으로 미래항공 모빌리티의 개발비용과 반복적인 설계 피드백 횟수를 줄일 수 있다. 두 번째는 상세설계단계를 위해 초기에 전반적인 형상과 중량 분포를 계산하는 "사이징"이다. 본 연구에서는 미래항공 모빌리티 설계를 위한 결정론적 사이징 프레임워크를 우선 제시하고, 이를 기반으로 하여 형상변수와 단순화된 수학적 해석 모듈에 존재하는 불확실성을 몬테카를로 시뮬레이션을 통해 사이징 프레임워크에 반영하였다. 불확실성을 고려한 사이징은 사이징 결과에 적절한 설계여유를 확보하고, 초기설계단계에서 불확실성에 대한 시스템의 반응을 이해할 수 있도록 하여 의사 결정론자가 이후 설계단계를 준비하는데 도움이 될 수 있다. 두 가지 개선된 프레임워크는 전기동력 수직이착륙기 형태의 가상의 미래항공 모빌리티 설계에 적용되었다. 예제 프로젝트는 제시된 두 방법론의 적용 및 결과분석에 대한 예제로서 이를 통해 방법론의 유효성을 확인할 수 있다. 불확실성 기반의 기술 포트폴리오 평가 프레임워크와 미래항공 모빌리티 사이징 프레임워크 두 가지 모두 범용적인 방법론으로서 제시된 예제뿐만 아니라 다양한 항공기 개발 프로그램에 적용할 수 있다.Chapter 1 Introduction 1 1.1. Background of the Research 1 1.1.1. Brief review of a design process 4 1.1.2. Importance of conceptual design phase 7 1.1.3. Primary tasks in the conceptual design phase 9 1.2. Previous studies concerning the primary tasks 13 1.3. Motivation and objectives 17 1.4. Outline of the dissertation 18 Chapter 2 Formulation of Assessment and Design Framework 19 2.1. Technology Portfolio Assessment 19 2.1.1. Overall Process 19 2.1.2. Impact of Technologies in System Level 22 2.1.3. Technology Compatibility & Interaction 25 2.1.4. Technology Portfolio Effect 29 2.1.5. Evaluation by Surrogate Model 32 2.1.6. Selection by Effectiveness 34 2.2. Sizing Framework for Advanced Air Mobility 37 2.2.1. Overall Process and Description 38 2.2.2. Rotor Aerodynamic Model 41 2.2.3. Wing and Fuselage Aerodynamic Model 45 2.2.4. Electric Propulsion System Sizing Model 48 2.2.5. Weight Estimation Model 56 2.2.6. Cost Estimation Model 60 2.2.7. Noise Model 67 Chapter 3 Uncertainty Environment 71 3.1. Types of Uncertainties 71 3.1.1. Aleatory Uncertainty 72 3.1.2. Epistemic Uncertainty 73 3.1.3. Other Uncertainties 74 3.1.4. Effect of Uncertainties 75 3.2. Uncertainties in Technology Portfolio Assessment Process 76 3.2.1. Uncertainty in technology impact and interaction factor 76 3.2.2. Uncertainty in surrogate model 82 3.3. Uncertainties in Conceptual Design Framework 85 3.3.1. Uncertainty in physical parameter variant 85 3.3.2. Uncertainty in simplified analysis models 86 3.4. Uncertainty Propagation: Monte Carlo Simulation 93 Chapter 4 Method Implementation 97 4.1. Uncertainty-based Technology Portfolio Assessment for eVTOL 97 4.1.1. Test Bed eVTOL 98 4.1.2. Technology Identification 101 4.1.3. Technology Impact 103 4.1.4. Generation of Technology Portfolio Candidates 106 4.1.5. Technology Portfolio Effect 108 4.1.6. Surrogate Model construction 109 4.1.7. Evaluation with MCS 114 4.1.8. Selection by Effectiveness 116 4.2. Sizing of eVTOL under uncertainties in conceptual design 135 4.2.1. Test Bed eVTOL 135 4.2.2. Uncertainty Identification 135 4.2.3. MCS process 138 4.2.4. Sensitivity Study 139 4.2.5. Hover Performance Analysis 151 4.2.6. Sizing for Mission Flight 163 Chapter 5 Conclusion 174 5.1. Summary 174 5.2. Originality and Contribution 177 5.3. Future Work Recommendation 180 References 183 국문 초록 197박

Development of a multi-objective optimization algorithm based on lichtenberg figures

This doctoral dissertation presents the most important concepts of multi-objective optimization and a systematic review of the most cited articles in the last years of this subject in mechanical engineering. The State of the Art shows a trend towards the use of metaheuristics and the use of a posteriori decision-making techniques to solve engineering problems. This fact increases the demand for algorithms, which compete to deliver the most accurate answers at the lowest possible computational cost. In this context, a new hybrid multi-objective metaheuristic inspired by lightning and Linchtenberg Figures is proposed. The Multi-objective Lichtenberg Algorithm (MOLA) is tested using complex test functions and explicit contrainted engineering problems and compared with other metaheuristics. MOLA outperformed the most used algorithms in the literature: NSGA-II, MOPSO, MOEA/D, MOGWO, and MOGOA. After initial validation, it was applied to two complex and impossible to be analytically evaluated problems. The first was a design case: the multi-objective optimization of CFRP isogrid tubes using the finite element method. The optimizations were made considering two methodologies: i) using a metamodel, and ii) the finite element updating. The last proved to be the best methodology, finding solutions that reduced at least 45.69% of the mass, 18.4% of the instability coefficient, 61.76% of the Tsai-Wu failure index and increased by at least 52.57% the natural frequency. In the second application, MOLA was internally modified and associated with feature selection techniques to become the Multi-objective Sensor Selection and Placement Optimization based on the Lichtenberg Algorithm (MOSSPOLA), an unprecedented Sensor Placement Optimization (SPO) algorithm that maximizes the acquired modal response and minimizes the number of sensors for any structure. Although this is a structural health monitoring principle, it has never been done before. MOSSPOLA was applied to a real helicopter’s main rotor blade using the 7 best-known metrics in SPO. Pareto fronts and sensor configurations were unprecedentedly generated and compared. Better sensor distributions were associated with higher hypervolume and the algorithm found a sensor configuration for each sensor number and metric, including one with 100% accuracy in identifying delamination considering triaxial modal displacements, minimum number of sensors, and noise for all blade sections.Esta tese de doutorado traz os conceitos mais importantes de otimização multi-objetivo e uma revisão sistemática dos artigos mais citados nos últimos anos deste tema em engenharia mecânica. O estado da arte mostra uma tendência no uso de meta-heurísticas e de técnicas de tomada de decisão a posteriori para resolver problemas de engenharia. Este fato aumenta a demanda sobre os algoritmos, que competem para entregar respostas mais precisas com o menor custo computacional possível. Nesse contexto, é proposta uma nova meta-heurística híbrida multi-objetivo inspirada em raios e Figuras de Lichtenberg. O Algoritmo de Lichtenberg Multi-objetivo (MOLA) é testado e comparado com outras metaheurísticas usando funções de teste complexas e problemas restritos e explícitos de engenharia. Ele superou os algoritmos mais utilizados na literatura: NSGA-II, MOPSO, MOEA/D, MOGWO e MOGOA. Após validação, foi aplicado em dois problemas complexos e impossíveis de serem analiticamente otimizados. O primeiro foi um caso de projeto: otimização multi-objetivo de tubos isogrid CFRP usando o método dos elementos finitos. As otimizações foram feitas considerando duas metodologias: i) usando um meta-modelo, e ii) atualização por elementos finitos. A última provou ser a melhor metodologia, encontrando soluções que reduziram pelo menos 45,69% da massa, 18,4% do coeficiente de instabilidade, 61,76% do TW e aumentaram em pelo menos 52,57% a frequência natural. Na segunda aplicação, MOLA foi modificado internamente e associado a técnicas de feature selection para se tornar o Seleção e Alocação ótima de Sensores Multi-objetivo baseado no Algoritmo de Lichtenberg (MOSSPOLA), um algoritmo inédito de Otimização de Posicionamento de Sensores (SPO) que maximiza a resposta modal adquirida e minimiza o número de sensores para qualquer estrutura. Embora isto seja um princípio de Monitoramento da Saúde Estrutural, nunca foi feito antes. O MOSSPOLA foi aplicado na pá do rotor principal de um helicóptero real usando as 7 métricas mais conhecidas em SPO. Frentes de Pareto e configurações de sensores foram ineditamente geradas e comparadas. Melhores distribuições de sensores foram associadas a um alto hipervolume e o algoritmo encontrou uma configuração de sensor para cada número de sensores e métrica, incluindo uma com 100% de precisão na identificação de delaminação considerando deslocamentos modais triaxiais, número mínimo de sensores e ruído para todas as seções da lâmina

Sensor Fusion in the Perception of Self-Motion

This dissertation has been written at the Max Planck Institute for Biological Cybernetics (Max-Planck-Institut für Biologische Kybernetik) in Tübingen in the department of Prof. Dr. Heinrich H. Bülthoff. The work has universitary support by Prof. Dr. Günther Palm (University of Ulm, Abteilung Neuroinformatik). Main evaluators are Prof. Dr. Günther Palm, Prof. Dr. Wolfgang Becker (University of Ulm, Sektion Neurophysiologie) and Prof. Dr. Heinrich Bülthoff.amp;lt;bramp;gt;amp;lt;bramp;gt; The goal of this thesis was to investigate the integration of different sensory modalities in the perception of self-motion, by using psychophysical methods. Experiments with healthy human participants were to be designed for and performed in the Motion Lab, which is equipped with a simulator platform and projection screen. Results from psychophysical experiments should be used to refine models of the multisensory integration process, with an mphasis on Bayesian (maximum likelihood) integration mechanisms.amp;lt;bramp;gt;amp;lt;bramp;gt; To put the psychophysical experiments into the larger framework of research on multisensory integration in the brain, results of neuroanatomical and neurophysiological experiments on multisensory integration are also reviewed

MULE DEER POPULATION DYNAMICS IN SPACE AND TIME: ECOLOGICAL MODELING TOOLS FOR MANAGING UNGULATES

Ecologists aim to understand and predict the effect of management actions on population dynamics of animals, a difficult task in highly variable environments. Mule deer (Odocoileus hemionus) occupy such variable environments and display volatile population dynamics, providing a challenging management scenario. I first investigate the ecological drivers of overwinter juvenile survival, the most variable life stage in this ungulate. I tested for both direct and indirect effects of spring and fall phenology on winter survival of 2,315 mule deer fawns from 1998 – 2011 across a wide range of environmental conditions in Idaho, USA. I showed that early winter precipitation and direct and indirect effects of spring and especially fall plant productivity (NDVI) accounted for 45% of observed variation in overwinter survival. I next develop predictive models of overwinter survival for 2,529 fawns within 11 Population Management Units in Idaho, 2003 – 2013. I used Bayesian hierarchical survival models to estimate survival from remotely-sensed measures of summer NDVI and winter snow conditions (MODIS snow and SNODAS). The multi-scale analysis produced well performing models, predicting out-of-sample data with a validation R2 of 0.66. Next, I ask how predation risk and deer density influences neonatal fawn survival. I developed a spatial coyote predation risk model and tested the effect on fawn mortality. I then regressed both total fawn mortality and coyote-caused mortality on mule deer density to test the predation-risk hypothesis that coyote predation risk increased as deer density increased as low predation risk habitats were filled, forcing maternal females to use high predation risk habitats. Fawn mortality did not increase with density, but coyote predation increased with increasing deer density, confirming density-dependence in fawn mortality was driven by coyote behavior, not density per se. Finally, I use integrated population models (IPM) to collate the previous findings into a model that simultaneously estimates all mule deer vital rates to test ecological questions concerning population drivers. I test whether density-dependence or environmental stochasticity (weather) drives mule deer population dynamics. The vital rate most influenced by density was recruitment, yet across most populations, weather was the predominant force affecting mule deer dynamics. These IPM’s will provide managers with a means to estimate population dynamics with precision and flexibility

Machine learning and data-driven techniques for verification and synthesis of cyber-physical systems

Safety and performance are the most important requirements for designing and manufacturing complex life-critical systems. Consider a self-driving car which is not equipped with certain safety functionalities. It can cause fatal accidents, severe injuries, or serious damages to the environment. Hence, rigorous analysis required to ensure the correctness of functionalities in many safety-critical applications. Model-based approaches for satisfying such requirements have been studied extensively in the literature. Unfortunately, a precise model of the system is not always available in many practical scenarios. Hence, in this thesis we focus on data-driven methods and machine learning techniques to tackle this challenge. First, we assume that only an incomplete parameterized model of the system is available. The main goal is to study formal verification of linear time-invariant systems with respect to a fragment of temporal logic specifications when only a partial knowledge of the model is available, i.e., a parameterized model of the system is known but the exact values of the parameters are unknown. We provide a probabilistic measure for the satisfaction of the specification by trajectories of the system under the influence of uncertainty. We assume that these specifications are expressed as signal temporal logic formulae and provide an approach that relies on gathering input-output data from the system. We employ Bayesian inference on the collected data to associate a notion of confidence with the satisfaction of the specification. Second, we assume that we do not have any knowledge about the model of the system and just have access to input-output data from the system. We study verification and synthesis problems for safety specifications over unknown discrete-time stochastic systems. When a model of the system is available, notion of barrier certificates have been successfully applied for ensuring the satisfaction of safety specifications. Here, we formulate the computation of barrier certificates as a robust convex program (RCP). Solving the acquired RCP is difficult in general because the model of the system that appears in one of the constraints of the RCP is unknown. We propose a data-driven approach that replaces the uncountable number of constraints in the RCP with a finite number of constraints by taking finitely many random samples from the trajectories of the system. We thus replace the original RCP with a scenario convex program (SCP) and show how to relate their optimizers. We guarantee that the solution of the SCP is a solution of the RCP with a priori guaranteed confidence when the number of samples is larger than a specific value. This provides a lower bound on the safety probability of the original unknown system together with a controller in the case of synthesis. Lastly, to address the high demand for data in our data-driven barrier-based approach, we propose three remedies. First, the wait-and-judge approach that checks a condition over the optimal value of the SCP using a fixed number of samples, ensuring a lower bound probability and the desired confidence for satisfying safety specifications. Second, the repetition-based scenario framework that iteratively solves the SCP with samples, checking feasibility and achieving the desired violation error. A safety condition is verified, enabling the computation of a lower bound for safety satisfaction. Third, the wait, judge, and repeat framework that solves the SCP iteratively until a feasibility condition, based on computed support constraints, is met. If the safety condition is satisfied, the system is considered safe with a lower bound probability determined using the optimizer of the successful iteration

Real-Time, Multiple Pan/Tilt/Zoom Computer Vision Tracking and 3D Positioning System for Unmanned Aerial System Metrology

The study of structural characteristics of Unmanned Aerial Systems (UASs) continues to be an important field of research for developing state of the art nano/micro systems. Development of a metrology system using computer vision (CV) tracking and 3D point extraction would provide an avenue for making these theoretical developments. This work provides a portable, scalable system capable of real-time tracking, zooming, and 3D position estimation of a UAS using multiple cameras. Current state-of-the-art photogrammetry systems use retro-reflective markers or single point lasers to obtain object poses and/or positions over time. Using a CV pan/tilt/zoom (PTZ) system has the potential to circumvent their limitations. The system developed in this paper exploits parallel-processing and the GPU for CV-tracking, using optical flow and known camera motion, in order to capture a moving object using two PTU cameras. The parallel-processing technique developed in this work is versatile, allowing the ability to test other CV methods with a PTZ system using known camera motion. Utilizing known camera poses, the object\u27s 3D position is estimated and focal lengths are estimated for filling the image to a desired amount. This system is tested against truth data obtained using an industrial system