Fusion Techniques for Reliable Information: A Survey <s> Introduction <s> Multisensor data fusion is an emerging technology applied to Department of Defense (DoD) areas such as automated target recognition, battlefield surveillance, and guidance and control of autonomous vehicles, and to non-DoD applications such as monitoring of complex machinery, medical diagnosis, and smart buildings. Techniques for multisensor data fusion are drawn from a wide range of areas including artificial intelligence, pattern recognition, statistical estimation and other areas. This paper provides a tutorial on data fusion, introducing data fusion applications, process models, and identification of applicable techniques. Comments are made on the state-of-the-art in data fusion. <s> BIB001 </s> Fusion Techniques for Reliable Information: A Survey <s> Introduction <s> Abstract Multi-sensor management concerns the control of environment perception activities by managing or coordinating the usage of multiple sensor resources. It is an emerging research area, which has become increasingly important in research and development of modern multi-sensor systems. This paper presents a comprehensive review of multi-sensor management in relation to multi-sensor information fusion, describing its place and role in the larger context, generalizing main problems from existing application needs, and highlighting problem solving methodologies. <s> BIB002 </s> Fusion Techniques for Reliable Information: A Survey <s> Introduction <s> Multisensor fusion and integration is a rapidly evolving research area and requires interdisciplinary knowledge in control theory, signal processing, artificial intelligence, probability and statistics, etc. The advantages gained through the use of redundant, complementary, or more timely information in a system can provide more reliable and accurate information. This paper provides an overview of current sensor technologies and describes the paradigm of multisensor fusion and integration as well as fusion techniques at different fusion levels. Applications of multisensor fusion in robotics, biomedical system, equipment monitoring, remote sensing, and transportation system are also discussed. Finally, future research directions of multisensor fusion technology including microsensors, smart sensors, and adaptive fusion techniques are presented. <s> BIB003 </s> Fusion Techniques for Reliable Information: A Survey <s> Introduction <s> This paper reviews the potential benefits that can be obtained by the implementation of data fusion in a multi-sensor environment. A thorough review of the commonly used data fusion frameworks is presented together with important factors that need to be considered during the development of an effective data fusion problem-solving strategy. A system-based approach is defined for the application of data fusion systems within engineering. Structured guidelines for users are proposed. <s> BIB004 </s> Fusion Techniques for Reliable Information: A Survey <s> Introduction <s> An overview of data fusion approaches is provided from the signal processing viewpoint. The general concept of data fusion is introduced, together with the related architectures, algorithms and performance aspects. Benefits of such an approach are highlighted and potential applications are identified. Case studies illustrate the merits of applying data fusion concepts in real world applications. <s> BIB005 </s> Fusion Techniques for Reliable Information: A Survey <s> Introduction <s> Data Fusion is a very broad interdisciplinary technology domain. It provides techniques and methods for; integrating information from multiple sources and using the complementarities of these detections to derive maximum information about the phenomenon being observed; analyzing and deriving the meaning of these observations and predicting possible consequences of the observed state of the environment; selecting the best course of action; and controlling the actions. Here, the focus is on the more mature phase of data fusion, namely the detection and identification/classification of phenomena being observed and exploitation of the related methods for Security-Related Civil Science and Technology (SST) applications. It is necessary to: expand on the data fusion methodology pertinent to Situation Monitoring, Incident Detection, Alert and Response Management; discuss some related Cognitive Engineering and visualization issues; provide an insight into the architectures and methodologies for building a data fusion system; discuss fusion approaches to image exploitation with emphasis on security applications; discuss novel distributed tracking approaches as a necessary step of situation monitoring and incident detection; and provide examples of real situations, in which data fusion can enhance incident detection, prevention and response capability. In order to give a logical presentation of the data fusion material, first the general concepts are highlighted (Fusion Methodology, Human Computer Interactions and Systems and Architectures), closing with several applications (Data Fusion for Imagery, Tracking and Sensor Fusion and Applications and Opportunities for Fusion). <s> BIB006
In pervasive and ubiquitous computing applications such as smart homes, offices, hospitals, and spaces BIB004 , BIB001 , BIB003 , BIB005 , BIB006 , BIB002 , some fusion techniques have been proposed to reduce uncertainty and achieve reliable data processing and analysis in a fusion system as shown in Figure 1 . However, it is still open problem to obtain the reliable contextual information in specific applications using the proposed fusion technique. First, no commonly accepted approaches, which can estimate uncertainty in a fusion system, exist. Second, raw-level data or input-output characteristics of each device may cause the total uncertainty of estimation or inference. Third, a higher inference can not correct possible errors that may occur in a lower level of data processing. And last, identification tasks and decision makings based on multiple classifications may suffer from the wrongly selected data set  ,  .