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.

This book includes papers from the section “Multisensor Information Fusion”, from Sensors between 2018 to 2019. It focuses on the latest research results of current multi-sensor fusion technologies and represents the latest research trends, including traditional information fusion technologies, estimation and filtering, and the latest research, artificial intelligence involving deep learning.

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.

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.

This book illustrates the benefits of sensor fusion by considering the characteristics of infrared, microwave, and millimeter-wave sensors, including the influence of the atmosphere on their performance. Applications that benefit from this technology include: vehicular traffic management, remote sensing, target classification and tracking- weather forecasting- military and homeland defense. Covering data fusion algorithms in detail, Klein includes a summary of the information required to implement each of the algorithms discussed, and outlines system application scenarios that may limit sensor size but that require high resolution data.

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

This textbook provides a comprehensive introduction to the theories and techniques of multi-sensor data fusion. It is aimed at advanced undergraduate and first-year graduate students in electrical engineering and computer science, as well as researchers and professional engineers. The book is intended to be self-contained. No previous knowledge of multi-sensor data fusion is assumed, although some familiarity with the basic tools of linear algebra, calculus and simple probability theory is recommended.

The definitive textbook and professional reference on Kalman Filtering – fully updated, revised, and expanded This book contains the latest developments in the implementation and application of Kalman filtering. Authors Grewal and Andrews draw upon their decades of experience to offer an in-depth examination of the subtleties, common pitfalls, and limitations of estimation theory as it applies to real-world situations. They present many illustrative examples including adaptations for nonlinear filtering, global navigation satellite systems, the error modeling of gyros and accelerometers, inertial navigation systems, and freeway traffic control. Kalman Filtering: Theory and Practice Using MATLAB, Fourth Edition is an ideal textbook in advanced undergraduate and beginning graduate courses in stochastic processes and Kalman filtering. It is also appropriate for self-instruction or review by practicing engineers and scientists who want to learn more about this important topic.

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.