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Uncertainty theories and multisensor data fusion
Addressing recent challenges and developments in this growing field, Multisensor Data Fusion Uncertainty Theory first discusses basic questions such as: Why and when is multiple sensor fusion necessary? How can the available measurements be characterized in such a case? What is the purpose and the specificity of information fusion processing in multiple sensor systems? Considering the different uncertainty formalisms, a set of coherent operators corresponding to the different steps of a complete fusion process is then developed, in order to meet the requirements identified in the first part of the book.
eBook
Advances in statistical multisource-multitarget information fusion
This is the sequel to the 2007 Artech House bestselling title, Statistical Multisource-Multitarget Information Fusion. That earlier book was a comprehensive resource for an in-depth understanding of finite-set statistics (FISST), a unified, systematic, and Bayesian approach to information fusion. The cardinalized probability hypothesis density (CPHD) filter, which was first systematically described in the earlier book, has since become a standard multitarget detection and tracking technique, especially in research and development.Since 2007, FISST has inspired a considerable amount of research, conducted in more than a dozen nations, and reported in nearly a thousand publications. This sequel addresses the most intriguing practical and theoretical advances in FISST, for the first time aggregating and systematizing them into a coherent, integrated, and deep-dive picture. Special emphasis is given to computationally fast exact closed-form implementation approaches. The book also includes the first complete and systematic description of RFS-based sensor/platform management and situation assessment.
eBook
Statistical multisource-multitarget information fusion
Information fusion is the process of gathering, filtering, correlating and integrating relevant information from various sources into one representational format. This work provides practitioners with an understanding of finite-set statistics (FISST). It helps professionals use FISST to create efficient information fusion systems.
eBook
Advances and challenges in multisensor data and information processing
Information fusion resulting from multi-source processing, often called multisensor data fusion when sensors are the main sources of information, is a relatively young (less than 20 years) technology domain. It provides techniques and methods for: Integrating data from multiple sources and using the complementarity of this data to derive maximum information about the phenomenon being observed; Analyzing and deriving the meaning of these observations; Selecting the best course of action; and Controlling the actions. Various sensors have been designed to detect some specific phenomena, but not others. Data fusion applications can combine synergically information from many sensors, including data provided by satellites and contextual and encyclopedic knowledge, to provide enhanced ability to detect and recognize anomalies in the environment, compared with conventional means. Data fusion is an integral part of multisensor processing, but it can also be applied to fuse non-sensor information (geopolitical, intelligence, etc.) to provide decision support for a timely and effective situation and threat assessment. One special field of application for data fusion is satellite imagery, which can provide extensive information over a wide area of the electromagnetic spectrum using several types of sensors (Visible, Infra-Red (IR), Thermal IR, Radar, Synthetic Aperture Radar (SAR), Polarimetric SAR (PolSAR), Hyperspectral...). Satellite imagery provides the coverage rate needed to identify and monitor human activities from agricultural practices (land use, crop types identification...) to defence-related surveillance (land/sea target detection and classification). By acquiring remotely sensed imagery over earth regions that land sensors cannot access, valuable information can be gathered for the defence against terrorism. This books deals with the following
research areas: Target recognition/classification and tracking; Sensor systems; Image processing; Remote sensing and remote control; Belief functions theory; and Situation assessment.
eBook
A review on wire-arc additive manufacturing: typical defects, detection approaches, and multisensor data fusion-based model
Wire-arc additive manufacturing (WAAM) technology integrates the characteristics of additive manufacturing and traditional welding technology. It has been found to be capable of forming large-scale metal components with low cost and higher deposition rates. However, it also has potential issues in morphological accuracy, microstructure, and properties due to the arc-based metallurgy mechanisms with complex thermal cycles. This article intends to give a detailed overview of the quality diagnosis and control of the WAAM process, including the formation mechanisms of typical defects, detection approaches, and detection challenges that remain in the WAAM industrial environments. Subsequently, a multisensor data fusion-based closed-loop quality control model is proposed. This model could provide a feasible solution to ensure the quality and deposition efficiency of WAAM parts in complex manufacturing conditions. Based on this model, a novel process named hybrid deposition and micro-rolling (HDMR) is introduced as the green transformation of traditional manufacturing approaches with higher energy efficiency and better quality.
Journal Article
Scientific Support for the Decision Making in the Security Sector
Today's security environment is characterized by deep uncertainty. Threats are being posed not only by adversary (political) forces but may also come from natural challenges (be it energy, water, ecology or whatever). The types of operations that our civil security and military forces find themselves in today comprise a wide variety of tasks. The success criteria for these operations are a safe/secure environment for local population and stable conditions for state building rather than hit-kill ratio's against adversaries - the criteria are soft and the many actors involved may have divergent if not opposing objectives. And where actors intentionally share common objectives, they come from different cultural and organizational backgrounds, and their systems and modus operandi (doctrine) have loose or no connectivity. Under these complex and uncertain conditions decision making is a challenging process. This publication reflect the initial state of a dialogue between specialists in security and specialists in mathematics, computer and information sciences on security topics. Papers included in this volume are naturally subdivided into four parts showing the wide future perspective for synthesis between science and security: Planning for Security; Mathematical, Computer and Information Sciences Methods for Security; Environmental Security; and Dynamic Optimization for Security.
eBook
Distributed Multisensor Data Fusion under Unknown Correlation and Data Inconsistency
The paradigm of multisensor data fusion has been evolved from a centralized architecture to a decentralized or distributed architecture along with the advancement in sensor and communication technologies. These days, distributed state estimation and data fusion has been widely explored in diverse fields of engineering and control due to its superior performance over the centralized one in terms of flexibility, robustness to failure and cost effectiveness in infrastructure and communication. However, distributed multisensor data fusion is not without technical challenges to overcome: namely, dealing with cross-correlation and inconsistency among state estimates and sensor data. In this paper, we review the key theories and methodologies of distributed multisensor data fusion available to date with a specific focus on handling unknown correlation and data inconsistency. We aim at providing readers with a unifying view out of individual theories and methodologies by presenting a formal analysis of their implications. Finally, several directions of future research are highlighted.
Journal Article
Multitemporal Feature-Level Fusion on Hyperspectral and LiDAR Data in the Urban Environment
Technological innovations and advanced multidisciplinary research increase the demand for multisensor data fusion in Earth observations. Such fusion has great potential, especially in the remote sensing field. One sensor is often insufficient in analyzing urban environments to obtain comprehensive results. Inspired by the capabilities of hyperspectral and Light Detection and Ranging (LiDAR) data in multisensor data fusion at the feature level, we present a novel approach to the multitemporal analysis of urban land cover in a case study in Høvik, Norway. Our generic workflow is based on bitemporal datasets; however, it is designed to include datasets from other years. Our framework extracts representative endmembers in an unsupervised way, retrieves abundance maps fed into segmentation algorithms, and detects the main urban land cover classes by implementing 2D ResU-Net for segmentation without parameter regularizations and with effective optimization. Such segmentation optimization is based on updating initial features and providing them for a second iteration of segmentation. We compared segmentation optimization models with and without data augmentation, achieving up to 11% better accuracy after segmentation optimization. In addition, a stable spectral library is automatically generated for each land cover class, allowing local database extension. The main product of the multitemporal analysis is a map update, effectively detecting detailed changes in land cover classes.
Journal Article
Multisensor Data Fusion in IoT Environments in Dempster–Shafer Theory Setting: An Improved Evidence Distance-Based Approach
In IoT environments, voluminous amounts of data are produced every single second. Due to multiple factors, these data are prone to various imperfections, they could be uncertain, conflicting, or even incorrect leading to wrong decisions. Multisensor data fusion has proved to be powerful for managing data coming from heterogeneous sources and moving towards effective decision-making. Dempster–Shafer (D–S) theory is a robust and flexible mathematical tool for modeling and merging uncertain, imprecise, and incomplete data, and is widely used in multisensor data fusion applications such as decision-making, fault diagnosis, pattern recognition, etc. However, the combination of contradictory data has always been challenging in D–S theory, unreasonable results may arise when dealing with highly conflicting sources. In this paper, an improved evidence combination approach is proposed to represent and manage both conflict and uncertainty in IoT environments in order to improve decision-making accuracy. It mainly relies on an improved evidence distance based on Hellinger distance and Deng entropy. To demonstrate the effectiveness of the proposed method, a benchmark example for target recognition and two real application cases in fault diagnosis and IoT decision-making have been provided. Fusion results were compared with several similar methods, and simulation analyses have shown the superiority of the proposed method in terms of conflict management, convergence speed, fusion results reliability, and decision accuracy. In fact, our approach achieved remarkable accuracy rates of 99.32% in target recognition example, 96.14% in fault diagnosis problem, and 99.54% in IoT decision-making application.
Journal Article