Schema AND Data: A Holistic Approach to Mapping, Resolution and Fusion in Information Integration
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Schema (genetic algorithms)
Sensor Fusion
Information integration
Information fusion
Abstract Under complex battlefield situation, the situation information is instantaneous and changeable. The uncertain information always causes difficulty in acquisition information and miscalculation, and lead to low efficiency and poor accuracy for aircraft situation awareness. In order to solve the problem, in this paper it proposes a deep-learning based multi-sensor situation awareness data fusion method. With data acquitted from multiple sensors in multi-band and multi-angle, it realizes multi-sensor information data fusion by comparing and analysing of two AI data fusion methods that one is based on classical evidence theory and the other is based on deep learning. The simulation results indicate that the deep-learning based data fusion method presents higher efficiency in dealing with the environment information fusion with large evidence conflict.
Battlefield
Sensor Fusion
Information fusion
Situation Awareness
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The problem of sensor integration and data fusion is addressed. We consider the problem of combining information from diversified sources in a coherent fashion. We assume that at the fusion, the information from various sensors may be available in different forms. For example, data from infrared (IR) sensors may be combined with range radar (RR) data, and further combined with visual images. In each case, the data and information from the different sensors are presented in a different format which may not be directly compatible for all sensors. Furthermore, the available information may be in the form of attributes and not dynamical measurements. A theory for sensor integration and data fusion that accommodates diversified sources of information is presented. Data (or, more generically, information) fusion may proceed at different levels, like the level of dynamics, the level of attributes, and the level of evidence. All different levels are considered and several practical examples of real world data fusion problems are discussed.
Sensor Fusion
Information integration
Information fusion
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Research the information fusion of intelligent car to improve the effective data quantity.This paper put forward an adaptive multi-sensor fusion algorithm.Through estimating the data fusion degree,filtering technology was used to improve the integration ability and increase the effective information after data fusion.Experiments show that this algorithm can greatly improve the diversity information fusion rate of multi-sensor and increase effective information quantity.
Sensor Fusion
Information fusion
Information integration
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Sensor Fusion
Information integration
Information fusion
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Information integration has been a subject of research for several decades and still remains a very active research area. Many new applications depend or benefit from large scale integration. Examples include large research projects in life sciences, need for data sharing among government agencies, reliance of corporations on business intelligence (which requires data integration from many heterogeneous sources), and integration of information on the web. The importance of information integration with uncertainty has been observed in recent years. Frequently, information from multiple sources are uncertain and possibly inconsistent. Further the process of integration often depends on approximate schema mappings, another source of uncertainty. An integration system is useful only to the extent that the information it produces can be trusted. Hence, providing a measure of certainty for integrated information is of crucial importance in many important applications.
Information integration
Information Sharing
Schema (genetic algorithms)
Enterprise information integration
Certainty
IDEF1X
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This article focuses on the core issue of data integration in order to analyze and elaborate the basic concepts and relationship between information system integration and data integration.It also introduces a classification model and the main difficulty of data integration,the methods of translation between the heterogeneity of data sources and the heterogeneous data sources.Finally,the future research in the field of data integration is predicted.
Enterprise information integration
Information integration
System Integration
IDEF1X
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In the field of information confusion, evidence theory takes advantage of its uncertainties. But in practical evidences are always mutually independent, However, multi-source information fusion is a comprehensive integration and then obtaining decision-making, sometimes the result of information fusion give us wrong conclusion. So an improved information fusion algorithm is proposed in this paper. It can heighten the information confusion reliability and accuracy in a practical example.
Information fusion
Confusion
Information integration
Sensor Fusion
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Multi-sensor data fusion is wide research branch in information field.As single data fusion algorithm always has some limitations,the integration of two or more data fusion algorithms is becoming a research interest.Advantages of data fusion are introduced;main characteristics,algorithms and applications of three data fusion model types(data level,characteristic level and decision level) are presented.Common data fusion algorithms are classified.Research developments of several data fusion integration algorithms are reviewed.Applications of data fusion technology are also discussed.
Sensor Fusion
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Multisensor data fusion and integration is a rapidly evolving research area that requires interdisciplinary knowledge in control theory, signal processing, artificial intelligence, probability and statistics, etc. Multisensor data fusion refers to the synergistic combination of sensory data from multiple sensors and related information to provide more reliable and accurate information than could be achieved using a single, independent sensor (Luo et al., 2007).Actually Multisensor data fusion is a multilevel, multifaceted process dealing with automatic detection, association, correlation, estimation, and combination of data from single and multiple information sources.The results of data fusion process help users make decisions in complicated scenarios.Integration of multiple sensor data was originally needed for military applications in ocean surveillance, air-to air and surface-to-air defence, or battlefield intelligence.More recently, multisensor data fusion has also included the nonmilitary fields of remote environmental sensing, medical diagnosis, automated monitoring of equipment, robotics, and automotive systems (Macci et al., 2008).The potential advantages of multisensor fusion and integration are redundancy, complementarity, timeliness, and cost of the information.The integration or fusion of redundant information can reduce overall uncertainty and thus serve to increase the accuracy with which the features are perceived by the system.Multiple sensors providing redundant information can also serve to increase reliability in the case of sensor error or failure.Complementary information from multiple sensors allows features in the environment to be perceived that are impossible to perceive using just the information from each individual sensor operating separately.(Luo et al., 2007) Besides, driving as one of our daily activities is a complex task involving a great amount of interaction between driver and vehicle.Drivers regularly share their attention among operating the vehicle, monitoring traffic and nearby obstacles, and performing secondary tasks such as conversing, adjusting comfort settings (e.g.temperature, radio.)The complexity of the task and uncertainty of the driving environment make driving a very dangerous task, as according to a study in the European member states, there are more than 1,200,000 traffic accidents a year with over 40,000 fatalities.This fact points up the growing demand for automotive safety systems, which aim for a significant contribution to the overall road safety (Tatschke et al., 2006).Therefore, recently, there are an increased number of research activities focusing on the Driver Assistance System (DAS) development in order Open Access Database www.intechweb.org
Sensor Fusion
Information integration
Data Processing
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Abstract The problem of sensor integration and data fusion is addressed. We consider the problem of combining information from diversified sources in a coherent fashion. We assume that information from various sensors may be available in different forms at the fusion. For example, data from infrared (IR) sensors may be combined with range radar (RR) data and further combined with visual images. In each case, data and information from different sensors are presented in a different format which may not be directly compatible for all sensors. Part of the available information may be in the form of attributes and part in the form of dynamical measurements. A generalized evidence processing theory and an architecture for sensor integration and data fusion that accommodates diversified sources of information are presented. Data (or, more generically, information) fusion may take place at different levels, such as the level of dynamics, the level of attributes, and the level of evidence. The common and different aspects of fusion at the different levels are investigated and several practical examples of real world data fusion problems are discussed.
Sensor Fusion
Information integration
Information fusion
Data Processing
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