Download or read book Dual Neural Extended Kalman Filtering Approach for Multirate Sensor Data Fusion with Industrial Applications written by Jingyi Wang and published by . This book was released on 2020 with total page 81 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Kalman filter algorithm and its variants have been widely applied to the multisensor data fusion problems to provide joint state estimation, which is more accurate than estimations from individual sensors. The performance of the Kalman filter based fusion relies on the accuracy of the models as well as process noise statistics. Deviations from correct system models and violations of noise assumptions may lead to unsatisfied sensor fusion results and even divergence. Two types of measurements are typically utilized to estimate process quality variables. One is frequent measurements, which are available at a fast and regular sampling rate but suffer from lower accuracy and higher measurement noises. The other type is infrequent measurements that are available at a slower sampling rate. The infrequent measurements, such as lab analysis results, have less availability but higher accuracy and are usually used as references to improve state estimation. The objective of this thesis is to develop new multirate sensor data fusion algorithms that can compensate for model inaccuracies and violations of noise assumption to improve the online sensor fusion performance. To fulfill this objective, a dual neural extended Kalman filter (DNEKF) algorithm is proposed by employing two neural networks to improve state estimation and output predictions. Using both frequent and infrequent measurements enables the DNEKF to provide more reliable training for the neural networks and hence to provide more robust and reliable sensor fusion results. Additionally, infrequent measurements are usually subject to irregular sampling rate and time-varying time delays. To address these problems while preserving the estimation accuracy, a fusion method that fuses frequent DNEKF estimates with infrequent estimates from the state model compensation NEKF (SNEKF) is proposed. In this approach, frequent and infrequent estimates are fused in the fusion center when the delayed infrequent measurements arrive. The weights and biases of the state model compensation neural network (SNN) are shared between the two synchronized estimation processes. In the primary separation cell (PSC) used for oil sands bitumen extraction, the interface level estimation is based on various sensors. Image processing based computer vision system, which uses a camera to capture sight glass vision frames, is considered to be the most accurate among these sensors. Although the accuracy of computer vision interface level estimation is high, its qualities are influenced by abnormalities, such as vision blocking, stains, and level transition between sight glasses. Under such abnormal scenarios, a sensor fusion strategy, which adaptively updates the fusion parameters, is proposed and integrated with the image processing based computer vision system. The performance of the proposed fault-tolerant multirate sensor fusion algorithms is demonstrated using numerical examples and case studies with industrial process data. The factory acceptance test (FAT) was conducted for the sensor fusion and computer vision integrated system in the computer process control (CPC) industrial research chair (IRC) lab under industrial environmental conditions and it demonstrated the improved estimation accuracy under various process abnormalities.
Download or read book Kalman Filtering and Neural Networks written by Simon Haykin and published by John Wiley & Sons. This book was released on 2004-03-24 with total page 302 pages. Available in PDF, EPUB and Kindle. Book excerpt: State-of-the-art coverage of Kalman filter methods for the design of neural networks This self-contained book consists of seven chapters by expert contributors that discuss Kalman filtering as applied to the training and use of neural networks. Although the traditional approach to the subject is almost always linear, this book recognizes and deals with the fact that real problems are most often nonlinear. The first chapter offers an introductory treatment of Kalman filters with an emphasis on basic Kalman filter theory, Rauch-Tung-Striebel smoother, and the extended Kalman filter. Other chapters cover: An algorithm for the training of feedforward and recurrent multilayered perceptrons, based on the decoupled extended Kalman filter (DEKF) Applications of the DEKF learning algorithm to the study of image sequences and the dynamic reconstruction of chaotic processes The dual estimation problem Stochastic nonlinear dynamics: the expectation-maximization (EM) algorithm and the extended Kalman smoothing (EKS) algorithm The unscented Kalman filter Each chapter, with the exception of the introduction, includes illustrative applications of the learning algorithms described here, some of which involve the use of simulated and real-life data. Kalman Filtering and Neural Networks serves as an expert resource for researchers in neural networks and nonlinear dynamical systems.
Download or read book Kalman Filtering and Information Fusion written by Hongbin Ma and published by Springer Nature. This book was released on 2019-11-27 with total page 295 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book addresses a key technology for digital information processing: Kalman filtering, which is generally considered to be one of the greatest discoveries of the 20th century. It introduces readers to issues concerning various uncertainties in a single plant, and to corresponding solutions based on adaptive estimation. Further, it discusses in detail the issues that arise when Kalman filtering technology is applied in multi-sensor systems and/or multi-agent systems, especially when various sensors are used in systems like intelligent robots, autonomous cars, smart homes, smart buildings, etc., requiring multi-sensor information fusion techniques. Furthermore, when multiple agents (subsystems) interact with one another, it produces coupling uncertainties, a challenging issue that is addressed here with the aid of novel decentralized adaptive filtering techniques.Overall, the book’s goal is to provide readers with a comprehensive investigation into the challenging problem of making Kalman filtering work well in the presence of various uncertainties and/or for multiple sensors/components. State-of-art techniques are introduced, together with a wealth of novel findings. As such, it can be a good reference book for researchers whose work involves filtering and applications; yet it can also serve as a postgraduate textbook for students in mathematics, engineering, automation, and related fields.To read this book, only a basic grasp of linear algebra and probability theory is needed, though experience with least squares, navigation, robotics, etc. would definitely be a plus.
Download or read book Kalman Filtering with Real Time Applications written by Charles K. Chui and published by Springer Science & Business Media. This book was released on 2013-03-09 with total page 202 pages. Available in PDF, EPUB and Kindle. Book excerpt: Kalman filtering is an optimal state estimation process applied to a dynamic system that involves random perturbations. More precisely, the Kalman filter gives a linear, unbiased, and min imum error variance recursive algorithm to optimally estimate the unknown state of a dynamic system from noisy data taken at discrete real-time intervals. It has been widely used in many areas of industrial and government applications such as video and laser tracking systems, satellite navigation, ballistic missile trajectory estimation, radar, and fue control. With the recent development of high-speed computers, the Kalman filter has become more use ful even for very complicated real-time applications. lnspite of its importance, the mathematical theory of Kalman filtering and its implications are not well understood even among many applied mathematicians and engineers. In fact, most prac titioners are just told what the filtering algorithms are without knowing why they work so well. One of the main objectives of this text is to disclose this mystery by presenting a fairly thor ough discussion of its mathematical theory and applications to various elementary real-time problems. A very elementary derivation of the filtering equations is fust presented. By assuming that certain matrices are nonsingular, the advantage of this approach is that the optimality of the Kalman filter can be easily understood. Of course these assump tions can be dropped by using the more well known method of orthogonal projection usually known as the innovations approach.
Download or read book Introduction and Implementations of the Kalman Filter written by Felix Govaers and published by BoD – Books on Demand. This book was released on 2019-05-22 with total page 130 pages. Available in PDF, EPUB and Kindle. Book excerpt: Sensor data fusion is the process of combining error-prone, heterogeneous, incomplete, and ambiguous data to gather a higher level of situational awareness. In principle, all living creatures are fusing information from their complementary senses to coordinate their actions and to detect and localize danger. In sensor data fusion, this process is transferred to electronic systems, which rely on some "awareness" of what is happening in certain areas of interest. By means of probability theory and statistics, it is possible to model the relationship between the state space and the sensor data. The number of ingredients of the resulting Kalman filter is limited, but its applications are not.
Download or read book Intuitive Understanding of Kalman Filtering with MATLAB written by Armando Barreto and published by CRC Press. This book was released on 2020-08-17 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The emergence of affordable micro sensors, such as MEMS Inertial Measurement Systems, are applied in embedded systems and Internet-of-Things devices. This has brought techniques such as Kalman Filtering, which are capable of combining information from multiple sensors or sources, to the interest of students and hobbyists. This book will explore the necessary background concepts, helping a much wider audience of readers develop an understanding and intuition that will enable them to follow the explanation for the Kalman Filtering algorithm. Key Features: Provides intuitive understanding of Kalman Filtering approach Succinct overview of concepts to enhance accessibility and appeal to a wide audience Interactive learning techniques with code examples Malek Adjouadi, PhD, is Ware Professor with the Department of Electrical and Computer Engineering at Florida International University, Miami. He received his PhD from the Electrical Engineering Department at the University of Florida, Gainesville. He is the Founding Director of the Center for Advanced Technology and Education funded by the National Science Foundation. His earlier work on computer vision to help persons with blindness led to his testimony to the U.S. Senate on the committee of Veterans Affairs on the subject of technology to help persons with disabilities. His research interests are in imaging, signal processing and machine learning, with applications in brain research and assistive technology. Armando Barreto, PhD, is Professor of the Electrical and Computer Engineering Department at Florida International University, Miami, as well as the Director of FIU’s Digital Signal Processing Laboratory, with more than 25 years of experience teaching DSP to undergraduate and graduate students. He earned his PhD in electrical engineering from the University of Florida, Gainesville. His work has focused on applying DSP techniques to the facilitation of human-computer interactions, particularly for the benefit of individuals with disabilities. He has developed human-computer interfaces based on the processing of signals and has developed a system that adds spatialized sounds to the icons in a computer interface to facilitate access by individuals with "low vision." With his research team, he has explored the use of Magnetic, Angular-Rate and Gravity (MARG) sensor modules and Inertial Measurement Units (IMUs) for human-computer interaction applications. He is a senior member of the Institute of Electrical and Electronics Engineers (IEEE) and the Association for Computing Machinery (ACM). Francisco R. Ortega, PhD, is an Assistant Professor at Colorado State University and Director of the Natural User Interaction Lab (NUILAB). Dr. Ortega earned his PhD in Computer Science (CS) in the field of Human-Computer Interaction (HCI) and 3D User Interfaces (3DUI) from Florida International University (FIU). He also held a position of Post-Doc and Visiting Assistant Professor at FIU. His main research area focuses on improving user interaction in 3DUI by (a) eliciting (hand and full-body) gesture and multimodal interactions, (b) developing techniques for multimodal interaction, and (c) developing interactive multimodal recognition systems. His secondary research aims to discover how to increase interest for CS in non-CS entry-level college students via virtual and augmented reality games. His research has resulted in multiple peer-reviewed publications in venues such as ACM ISS, ACM SUI, and IEEE 3DUI, among others. He is the first-author of the CRC Press book Interaction Design for 3D User Interfaces: The World of Modern Input Devices for Research, Applications and Game Development. Nonnarit O-larnnithipong, PhD, is an Instructor at Florida International University. Dr. O-larnnithipong earned his PhD in Electrical Engineering, majoring in Digital Signal Processing from Florida International University (FIU). He also held a position of Post-Doctoral Associate at FIU in 2019. His research has focused on (1) implementing the sensor fusion algorithm to improve orientation measurement using MEMS inertial and magnetic sensors and (2) developing a 3D hand motion tracking system using Inertial Measurement Units (IMUs) and infrared cameras. His research has resulted in multiple peer-reviewed publications in venues such as HCI-International and IEEE Sensors.
Download or read book Kalman Filtering for Multi sensor Data Fusion an Application to Autonomous Navigation written by T. Coianiz and published by . This book was released on 1993 with total page 12 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Extended Kalman Filter Sensor Fusion Signals of Nonlinear Dynamic Systems written by Defence Research Establishment Ottawa and published by . This book was released on 2001 with total page 20 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Applications of Neural Networks Using Extended Kalman Filtering written by Mark William Owen and published by . This book was released on 1997 with total page 310 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Advances in Aerospace Guidance Navigation and Control written by Bogusław Dołęga and published by Springer. This book was released on 2017-12-15 with total page 735 pages. Available in PDF, EPUB and Kindle. Book excerpt: The first three CEAS (Counsil of European Aerospace Societies) Specialist Conferences on Guidance, Navigation and Control (CEAS EuroGNC) were held in Munich, Germany in 2011, in Delft, Netherlands in 2013 and in Toulouse, France in 2017. The Warsaw University of Technology (WUT) and the Rzeszow University of Technology (RzUT) accepted the challenge of jointly organizing the 4th edition. The conference aims to promote scientific and technical excellence in the fields of Guidance, Navigation and Control (GNC) in aerospace and other fields of technology. The Conference joins together the industry with the academia research. This book covers four main topics: Guidance and Control, Control Theory Application, Navigation, UAV Control and Dynamic. The papers included focus on the most advanced and actual topics in guidance, navigation and control research areas: · Control theory, analysis, and design · ; Novel navigation, estimation, and tracking methods · Aircraft, spacecraft, missile and UAV guidance, navigation, and control · Flight testing and experimental results · Intelligent control in aerospace applications · Aerospace robotics and unmanned/autonomous systems · Sensor systems for guidance, navigation and control · Guidance, navigation, and control concepts in air traffic control systems For the 4th CEAS Specialist Conference on Guidance, Navigation and Control the International Technical Committee established a formal review process. Each paper was reviewed in compliance with good journal practices by independent and anonymous reviewers. At the end of the review process papers were selected for publication in this book.
Download or read book Distributed Adaptive High gain Extended Kalman Filtering for Nonlinear Systems written by Mohammad Rashedi and published by . This book was released on 2016 with total page 150 pages. Available in PDF, EPUB and Kindle. Book excerpt: Recently, increasing attention has been given to the theoretical and practical analysis of large-scale networked systems. Large-scale systems are usually composed of several interconnected subsystems connected through material and energy flows. Due to the scale of these systems and the interactions among subsystems, the design of appropriate process monitoring and control systems is challenging. To handle the scale and interactions of large-scale networked systems in process monitoring and control, distributed predictive control and distributed moving horizon estimation approaches have been developed. The distributed framework can improve the performance of the decentralized network and outperform the centralized framework in terms of fault tolerance. Most of the existing distributed control and process monitoring strategies require the availability of the state measurements of all subsystems; however this requirement may not be satisfied in many applications. In this thesis, we propose a distributed adaptive high-gain extended Kalman filtering approach for nonlinear systems. Specifically, we consider a class of nonlinear systems that are composed of several subsystems interacting with each other via their states. In the proposed approach, an adaptive high-gain extended Kalman filter is designed for each subsystem. The distributed filters communicate with each other to exchange subsystems' estimates. First, assuming continuous communication among the distributed filters, an implementation strategy which specifies how the distributed filters should communicate is designed and the detailed design of the subsystem filter is described. Second, we consider the case where the subsystem filters communicate to exchange information at discrete-time instants. Following this, the problem of time-varying delays and data losses in communications between subsystems' estimators is considered. For these two latter cases, a state predictor is used in each subsystem filter to provide predictions of the states of other subsystems. Also, to reduce the number of information transmission among the filters and prevent data trafficking, a triggered communication strategy is developed. The stability properties of the proposed distributed estimation schemes with the described communication types are analyzed. Finally, the effectiveness and applicability of the proposed schemes are illustrated via the applications to simulated chemical processes and a Three-Tank experimental system.
Download or read book Optimal Sampled Data Control Systems written by Tongwen Chen and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 376 pages. Available in PDF, EPUB and Kindle. Book excerpt: Among the many techniques for designing linear multivariable analogue controllers, the two most popular optimal ones are H2 and H-infinity optimization. The fact that most new industrial controllers are digital provides strong motivation for adapting or extending these techniques to digital control systems. This book, now available as a corrected reprint, attempts to do so. Part I presents two indirect methods of sampled-data controller design: These approaches include approximations to a real problem, which involves an analogue plant, continuous-time performance specifications, and a sampled-data controller. Part II proposes a direct attack in the continuous-time domain, where sampled-data systems are time-varying. The findings are presented in forms that can readily be programmed in, e.g., MATLAB.
Download or read book Multisensor Data Fusion written by David Hall and published by CRC Press. This book was released on 2001-06-20 with total page 564 pages. Available in PDF, EPUB and Kindle. Book excerpt: The emerging technology of multisensor data fusion has a wide range of applications, both in Department of Defense (DoD) areas and in the civilian arena. The techniques of multisensor data fusion draw from an equally broad range of disciplines, including artificial intelligence, pattern recognition, and statistical estimation. With the rapid evolut
Download or read book Soft Sensors for Monitoring and Control of Industrial Processes written by Luigi Fortuna and published by Springer Science & Business Media. This book was released on 2007-05-31 with total page 284 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book reviews current design paths for soft sensors, and guides readers in evaluating different choices. The book presents case studies resulting from collaborations between the authors and industrial partners. The solutions presented, some of which are implemented on-line in industrial plants, are designed to cope with a wide range of applications from measuring system backup and what-if analysis through real-time prediction for plant control to sensor diagnosis and validation.
Download or read book Applied Stochastic Differential Equations written by Simo Särkkä and published by Cambridge University Press. This book was released on 2019-05-02 with total page 327 pages. Available in PDF, EPUB and Kindle. Book excerpt: With this hands-on introduction readers will learn what SDEs are all about and how they should use them in practice.
Download or read book International Aerospace Abstracts written by and published by . This book was released on 1998 with total page 980 pages. Available in PDF, EPUB and Kindle. Book excerpt:
