Read Books Online and Download eBooks, EPub, PDF, Mobi, Kindle, Text Full Free.
Probabilistic Framework For Sensor Management
Download Probabilistic Framework For Sensor Management full books in PDF, epub, and Kindle. Read online Probabilistic Framework For Sensor Management ebook anywhere anytime directly on your device. Fast Download speed and no annoying ads. We cannot guarantee that every ebooks is available!
Book Synopsis Probabilistic Framework for Sensor Management by : Marco Huber
Download or read book Probabilistic Framework for Sensor Management written by Marco Huber and published by KIT Scientific Publishing. This book was released on 2009 with total page 184 pages. Available in PDF, EPUB and Kindle. Book excerpt: A probabilistic sensor management framework is introduced, which maximizes the utility of sensor systems with many different sensing modalities by dynamically configuring the sensor system in the most beneficial way. For this purpose, techniques from stochastic control and Bayesian estimation are combined such that long-term effects of possible sensor configurations and stochastic uncertainties resulting from noisy measurements can be incorporated into the sensor management decisions.
Book Synopsis Aviation and Human Factors by : Jose Sanchez-Alarcos
Download or read book Aviation and Human Factors written by Jose Sanchez-Alarcos and published by CRC Press. This book was released on 2019-06-19 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Air safety is right now at a point where the chances of being killed in an aviation accident are far lower than the chances to winning a jackpot in any of the major lotteries. However, keeping or improving that performance level requires a critical analysis of some events that, despite scarce, point to structural failures in the learning process. The effect of these failures could increase soon if there is not a clear and right development path. This book tries to identify what is wrong, why there are things to fix, and some human factors principles to keep in aircraft design and operations. Features Shows, through different events, how the system learns through technology, practices, and regulations and the pitfalls of that learning process Discusses the use of information technology in safety-critical environments and why procedural knowledge is not enough Presents air safety management as a successful process, but at the same time, failures coming from technological and organizational features are shown Offers ways to improve from the human factors side by getting the right lessons from recent events
Book Synopsis Probabilistic Learning for Analysis of Sensor-based Human Activity Data by : Jonathan Hutchins
Download or read book Probabilistic Learning for Analysis of Sensor-based Human Activity Data written by Jonathan Hutchins and published by . This book was released on 2010 with total page 245 pages. Available in PDF, EPUB and Kindle. Book excerpt: As sensors that measure daily human activity become increasingly affordable and ubiquitous, there is a corresponding need for algorithms that unearth useful information from the resulting sensor observations. Many of these sensors record a time series of counts reflecting two behaviors: 1) the underlying hourly, daily, and weekly rhythms of natural human activity, and 2) bursty periods of unusual behavior. This dissertation explores a probabilistic framework for human-generated count data that (a) models the underlying recurrent patterns and (b) simultaneously separates and characterizes unusual activity via a Poisson-Markov model. The problems of event detection and characterization using real world, noisy sensor data with significant portions of data missing and corrupted measurements due to sensor failure are investigated. The framework is extended in order to perform higher level inferences, such as linking event models in a multi-sensor building occupancy model, and incorporating the occupancy measurement from loop detectors (in addition to the count measurement) to apply the model to problems in transportation research.
Book Synopsis Sensors & Symbols: An Integrated Framework by :
Download or read book Sensors & Symbols: An Integrated Framework written by and published by . This book was released on 1999 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The goal of this effort was to provide a unified probabilistic framework that integrates symbolic and sensory reasoning. Such a framework would allow sensor data to be analyzed in terms of high-level symbolic models. It will also allow the results of high-level analysis to guide the low-level sensor interpretation task and to help in resolving ambiguities in the sensor data. Our approach was based on the framework of probabilistic graphical models, which allows us to build systems that learn and reason with complex models, encompassing both low-level continuous sensor data and high-level symbolic concepts. Over the five years of the project, we explored two main thrusts: Inference and learning in hybrid and temporal Bayesian networks Mapping and modeling of 3D physical environments. Our progress on each of these two directions is detailed in the attached report.
Book Synopsis Physics-Based Probabilistic Motion Compensation of Elastically Deformable Objects by : Evgeniya Ballmann
Download or read book Physics-Based Probabilistic Motion Compensation of Elastically Deformable Objects written by Evgeniya Ballmann and published by KIT Scientific Publishing. This book was released on 2014-07-30 with total page 244 pages. Available in PDF, EPUB and Kindle. Book excerpt: A predictive tracking approach and a novel method for visual motion compensation are introduced, which accurately reconstruct and compensate the deformation of the elastic object, even in the case of complete measurement information loss. The core of the methods involves a probabilistic physical model of the object, from which all other mathematical models are systematically derived. Due to flexible adaptation of the models, the balance between their complexity and their accuracy is achieved.
Book Synopsis Sensor Management for Target Tracking Applications by : Per Boström-Rost
Download or read book Sensor Management for Target Tracking Applications written by Per Boström-Rost and published by Linköping University Electronic Press. This book was released on 2021-04-12 with total page 61 pages. Available in PDF, EPUB and Kindle. Book excerpt: Many practical applications, such as search and rescue operations and environmental monitoring, involve the use of mobile sensor platforms. The workload of the sensor operators is becoming overwhelming, as both the number of sensors and their complexity are increasing. This thesis addresses the problem of automating sensor systems to support the operators. This is often referred to as sensor management. By planning trajectories for the sensor platforms and exploiting sensor characteristics, the accuracy of the resulting state estimates can be improved. The considered sensor management problems are formulated in the framework of stochastic optimal control, where prior knowledge, sensor models, and environment models can be incorporated. The core challenge lies in making decisions based on the predicted utility of future measurements. In the special case of linear Gaussian measurement and motion models, the estimation performance is independent of the actual measurements. This reduces the problem of computing sensing trajectories to a deterministic optimal control problem, for which standard numerical optimization techniques can be applied. A theorem is formulated that makes it possible to reformulate a class of nonconvex optimization problems with matrix-valued variables as convex optimization problems. This theorem is then used to prove that globally optimal sensing trajectories can be computed using off-the-shelf optimization tools. As in many other fields, nonlinearities make sensor management problems more complicated. Two approaches are derived to handle the randomness inherent in the nonlinear problem of tracking a maneuvering target using a mobile range-bearing sensor with limited field of view. The first approach uses deterministic sampling to predict several candidates of future target trajectories that are taken into account when planning the sensing trajectory. This significantly increases the tracking performance compared to a conventional approach that neglects the uncertainty in the future target trajectory. The second approach is a method to find the optimal range between the sensor and the target. Given the size of the sensor's field of view and an assumption of the maximum acceleration of the target, the optimal range is determined as the one that minimizes the tracking error while satisfying a user-defined constraint on the probability of losing track of the target. While optimization for tracking of a single target may be difficult, planning for jointly maintaining track of discovered targets and searching for yet undetected targets is even more challenging. Conventional approaches are typically based on a traditional tracking method with separate handling of undetected targets. Here, it is shown that the Poisson multi-Bernoulli mixture (PMBM) filter provides a theoretical foundation for a unified search and track method, as it not only provides state estimates of discovered targets, but also maintains an explicit representation of where undetected targets may be located. Furthermore, in an effort to decrease the computational complexity, a version of the PMBM filter which uses a grid-based intensity to represent undetected targets is derived.
Book Synopsis Foundations and Applications of Sensor Management by : Alfred Olivier Hero
Download or read book Foundations and Applications of Sensor Management written by Alfred Olivier Hero and published by Springer Science & Business Media. This book was released on 2007-10-23 with total page 317 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers control theory signal processing and relevant applications in a unified manner. It introduces the area, takes stock of advances, and describes open problems and challenges in order to advance the field. The editors and contributors to this book are pioneers in the area of active sensing and sensor management, and represent the diverse communities that are targeted.
Book Synopsis Distributed Sensor Networks by : S. Sitharama Iyengar
Download or read book Distributed Sensor Networks written by S. Sitharama Iyengar and published by CRC Press. This book was released on 2004-12-29 with total page 1142 pages. Available in PDF, EPUB and Kindle. Book excerpt: The vision of researchers to create smart environments through the deployment of thousands of sensors, each with a short range wireless communications channel and capable of detecting ambient conditions such as temperature, movement, sound, light, or the presence of certain objects is becoming a reality. With the emergence of high-speed networks an
Book Synopsis Linear Estimation in Interconnected Sensor Systems with Information Constraints by : Reinhardt, Marc
Download or read book Linear Estimation in Interconnected Sensor Systems with Information Constraints written by Reinhardt, Marc and published by KIT Scientific Publishing. This book was released on 2015-04-15 with total page 262 pages. Available in PDF, EPUB and Kindle. Book excerpt: A ubiquitous challenge in many technical applications is to estimate an unknown state by means of data that stems from several, often heterogeneous sensor sources. In this book, information is interpreted stochastically, and techniques for the distributed processing of data are derived that minimize the error of estimates about the unknown state. Methods for the reconstruction of dependencies are proposed and novel approaches for the distributed processing of noisy data are developed.
Book Synopsis Optimal Sequence-Based Control of Networked Linear Systems by : Fischer, Joerg
Download or read book Optimal Sequence-Based Control of Networked Linear Systems written by Fischer, Joerg and published by KIT Scientific Publishing. This book was released on 2015-01-12 with total page 184 pages. Available in PDF, EPUB and Kindle. Book excerpt: In Networked Control Systems (NCS), components of a control loop are connected by data networks that may introduce time-varying delays and packet losses into the system, which can severly degrade control performance. Hence, this book presents the newly developed S-LQG (Sequence-Based Linear Quadratic Gaussian) controller that combines the sequence-based control method with the well-known LQG approach to stochastic optimal control in order to compensate for the network-induced effects.
Book Synopsis Probabilistic Framework for Person Tracking and Classification in Security Assistance Systems by : Monika Wieneke
Download or read book Probabilistic Framework for Person Tracking and Classification in Security Assistance Systems written by Monika Wieneke and published by . This book was released on 2013-04-05 with total page 288 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis On Informative Path Planning for Tracking and Surveillance by : Per Boström-Rost
Download or read book On Informative Path Planning for Tracking and Surveillance written by Per Boström-Rost and published by Linköping University Electronic Press. This book was released on 2019-05-23 with total page 86 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis studies a class of sensor management problems called informative path planning (IPP). Sensor management refers to the problem of optimizing control inputs for sensor systems in dynamic environments in order to achieve operational objectives. The problems are commonly formulated as stochastic optimal control problems, where to objective is to maximize the information gained from future measurements. In IPP, the control inputs affect the movement of the sensor platforms, and the goal is to compute trajectories from where the sensors can obtain measurements that maximize the estimation performance. The core challenge lies in making decisions based on the predicted utility of future measurements. In linear Gaussian settings, the estimation performance is independent of the actual measurements. This means that IPP becomes a deterministic optimal control problem, for which standard numerical optimization techniques can be applied. This is exploited in the first part of this thesis. A surveillance application is considered, where a mobile sensor is gathering information about features of interest while avoiding being tracked by an adversarial observer. The problem is formulated as an optimization problem that allows for a trade-off between informativeness and stealth. We formulate a theorem that makes it possible to reformulate a class of nonconvex optimization problems with matrix-valued variables as convex optimization problems. This theorem is then used to prove that the seemingly intractable IPP problem can be solved to global optimality using off-the-shelf optimization tools. The second part of this thesis considers tracking of a maneuvering target using a mobile sensor with limited field of view. The problem is formulated as an IPP problem, where the goal is to generate a sensor trajectory that maximizes the expected tracking performance, captured by a measure of the covariance matrix of the target state estimate. When the measurements are nonlinear functions of the target state, the tracking performance depends on the actual measurements, which depend on the target’s trajectory. Since these are unavailable in the planning stage, the problem becomes a stochastic optimal control problem. An approximation of the problem based on deterministic sampling of the distribution of the predicted target trajectory is proposed. It is demonstrated in a simulation study that the proposed method significantly increases the tracking performance compared to a conventional approach that neglects the uncertainty in the future target trajectory.
Author : Publisher :CRC Press ISBN 13 :1135439621 Total Pages :1142 pages Book Rating :4.1/5 (354 download)
Download or read book written by and published by CRC Press. This book was released on with total page 1142 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Proceedings of the 2009 Joint Workshop of Fraunhofer IOSB and Institute for Anthropomatics, Vision and Fusion Laboratory by : Jürgen Beyerer
Download or read book Proceedings of the 2009 Joint Workshop of Fraunhofer IOSB and Institute for Anthropomatics, Vision and Fusion Laboratory written by Jürgen Beyerer and published by KIT Scientific Publishing. This book was released on 2014-10-16 with total page 258 pages. Available in PDF, EPUB and Kindle. Book excerpt: The joint workshop of the Fraunhofer Institute of Optronics, System Technologies and Image Exploitation IOSB, Karlsruhe, and the Vision and Fusion Laboratory (Institute for Anthropomatics, Karlsruhe Institute of Technology (KIT)), is organized annually since 2005 with the aim to report on the latest research and development findings of the doctoral students of both institutions. This book provides a collection of 16 technical reports on the research results presented on the 2009 workshop.
Book Synopsis Learning Dynamic Systems for Intention Recognition in Human-Robot-Cooperation by : Peter Krauthausen
Download or read book Learning Dynamic Systems for Intention Recognition in Human-Robot-Cooperation written by Peter Krauthausen and published by KIT Scientific Publishing. This book was released on 2014-07-31 with total page 240 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis is concerned with intention recognition for a humanoid robot and investigates how the challenges of uncertain and incomplete observations, a high degree of detail of the used models, and real-time inference may be addressed by modeling the human rationale as hybrid, dynamic Bayesian networks and performing inference with these models. The key focus lies on the automatic identification of the employed nonlinear stochastic dependencies and the situation-specific inference.
Book Synopsis Deterministic Sampling for Nonlinear Dynamic State Estimation by : Gilitschenski, Igor
Download or read book Deterministic Sampling for Nonlinear Dynamic State Estimation written by Gilitschenski, Igor and published by KIT Scientific Publishing. This book was released on 2016-04-19 with total page 198 pages. Available in PDF, EPUB and Kindle. Book excerpt: The goal of this work is improving existing and suggesting novel filtering algorithms for nonlinear dynamic state estimation. Nonlinearity is considered in two ways: First, propagation is improved by proposing novel methods for approximating continuous probability distributions by discrete distributions defined on the same continuous domain. Second, nonlinear underlying domains are considered by proposing novel filters that inherently take the underlying geometry of these domains into account.
Book Synopsis State Estimation for Distributed Systems with Stochastic and Set-membership Uncertainties by : Noack, Benjamin
Download or read book State Estimation for Distributed Systems with Stochastic and Set-membership Uncertainties written by Noack, Benjamin and published by KIT Scientific Publishing. This book was released on 2014-01-02 with total page 292 pages. Available in PDF, EPUB and Kindle. Book excerpt: State estimation techniques for centralized, distributed, and decentralized systems are studied. An easy-to-implement state estimation concept is introduced that generalizes and combines basic principles of Kalman filter theory and ellipsoidal calculus. By means of this method, stochastic and set-membership uncertainties can be taken into consideration simultaneously. Different solutions for implementing these estimation algorithms in distributed networked systems are presented.
