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With the proliferation of sensors on platforms like battle ships and aircraft, the information to be handled by the battlefield commanders has significantly increased in the recent time. From a deluge of information flowing from sensors, the battlefield commander is required to make situation assessment in real-time and take appropriate action. Recent studies by cognitive scientists have indicated that decision making by individuals as well as a team suffer from several biases. For these two reasons, the battlefield commanders need assistance of real-time information fusion systems to take objective assessment of highly dynamic battle situation in real-time information fusion systems to take objective assessment of a highly dynamic battle situation in real-time. The real-time information fusion systems at a single platform level as well as that applicable for geographically distributed platforms is discussed in detail in this paper. It was concluded that by carrying out these activities at the platform level as well as at 'global' level involving several platforms, the limitations in performance of any sensor due to propagation effects or due to enemy counter measures can be significantly minimised or totally eliminated. At the same time the functional effectiveness of each sensor onboard different platforms, becomes better than when it had to operate autonomously within the real-time information fusion facility. By carrying out global real-time information fusion activity in a theatre of war, all the platforms operating in the area will have the benefit of the best sensor in that area on each aspect of the capability. A few examples of real-time information fusion system are also discussed.

In the heat of battle, a split second can be all the time a military commander has to give orders. Information fusion technology enables commanders to lead decisively and confidently during active conflict. Masses of live information are instantaneously integrated to create a coherent and precise picture of a rapidly evolving situation. This book brings together an international panel of leading experts that give a fresh and cohesive perspective on this technology's models, methods, mathematics, and computer systems. It considers the human factors key to the automated analysis process. Providi

This study introduces new information fusion algorithms to enhance disease surveillance systems with Bayesian decision support capabilities. A detection system was built and tested using chief complaints from emergency department visits, International Classification of Diseases Revision 9 (ICD-9) codes from records of outpatient visits to civilian and military facilities, and influenza surveillance data from health departments in the National Capital Region (NCR). Data anomalies were identified and distribution of time offsets between events in the multiple data streams were established. The Bayesian Network was built to fuse data from multiple sources and identify influenza-like epidemiologically relevant events. Results showed increased specificity compared with the alerts generated by temporal anomaly detection algorithms currently deployed by NCR health departments. Further research should be done to investigate correlations between data sources for efficient fusion of the collected data.

This state-of-the-art resource brings together the latest findings from the cross-fertilization of signal processing, machine learning and computer science. The emphasis is on demonstrating synergy of different signal processing methods with knowledge extraction and heterogeneous information fusion. Issues related to the processing of signals with low signal-to-noise ratio, solving real-world multi-channel problems, and using adaptive techniques where nonstationarity, uncertainty and complexity play major roles are addressed. Particular methods include Independent Component Analysis, Support Vector Machines, Distributed and Collaborative Adaptive Filtering, Empirical Mode Decomposition, Self Organizing Maps, Fuzzy Logic, Evolutionary Algorithms and several others used frequently in these fields. Also included are both important and novel applications from telecommunications, renewable energy and biomedical engineering. Signal Processing Techniques for Knowledge Extraction and Information Fusion which proposes new techniques for extracting knowledge based on combining heterogeneous information sources is an excellent reference for professionals in signal and image processing, machine learning, data and sensor fusion, computational intelligence, knowledge discovery, pattern recognition, and environmental science and engineering.

With the continuous development of information technology in recent years, information fusion technology, which originated from military applications, plays an important role in various fields. In addition, the rapidly increasing amount of data and the changing lifestyles of people in the information age are affecting the development of information fusion technology. More experts and scholars have focused their attention on the research of image or audio and video fusion or distributed fusion technology. This article summarizes the origin and development of information fusion technology and typical algorithms, as well as the future development trends and challenges of information fusion technology.

Information fusion is a research area which focuses on how to combine information from many different sources to support decision making. Commonly used information fusion systems are often complex and used in military and crises management domains. The focus of information fusion research so far has been mainly on the technological aspects. There is still a lack of understanding relevant user aspects that affect the information fusion systems as a whole. This paper presents a framework of HCI issues which considers users as embedded in the context of information fusion systems. The framework aims at providing insights regarding factors that affect user interaction to inform the development of future information fusion systems. Design considerations are presented together with a heuristic evaluation of an information fusion prototype.

Information fusion is a research area which focuses on how to combine information from many different sources to support decision making. Commonly used information fusion systems are often complex and used in military and crises management domains. The focus of information fusion research so far has been mainly on the technological aspects. There is still a lack of understanding relevant user aspects that affect the information fusion systems as a whole. This paper presents a framework of HCI issues which considers users as embedded in the context of information fusion systems. The framework aims at providing insights regarding factors that affect user interaction to inform the development of future information fusion systems. Design considerations are presented together with a heuristic evaluation of an information fusion prototype.

In recent years, the usage of unmanned aircraft systems (UAS) for security-related purposes has increased, ranging from military applications to different areas of civil protection. The deployment of UAS can support security forces in achieving an enhanced situational awareness. However, in order to provide useful input to a situational picture, sensor data provided by UAS has to be integrated with information about the area and objects of interest from other sources. The aim of this study is to design a high-level data fusion component combining probabilistic information processing with logical and probabilistic reasoning, to support human operators in their situational awareness and improving their capabilities for making efficient and effective decisions. To this end, a fusion component based on the ISR (Intelligence, Surveillance and Reconnaissance) Analytics Architecture (ISR-AA) [1] is presented, incorporating an object-oriented world model (OOWM) for information integration, an expressive knowledge model and a reasoning component for detection of critical events. Approaches for translating the information contained in the OOWM into either an ontology for logical reasoning or a Markov logic network for probabilistic reasoning are presented.

Information Fusion coordinates large-volume data processing machines to address user needs. Users expect a situational picture to extend their ability of sensing events, movements, and activities. Typically, data is collected and processed for object location (e.g. target identification) and movement (e.g. tracking); however, high-level reasoning or situational understanding depends on the spatial, cultural, and political effects. In this paper, we explore opportunities where information fusion can aid in the selection and processing of the data for enhanced tacit knowledge understanding by (1) display fusion for data presentation (e.g. cultural segmentation), (2) interactive fusion to allow the user to inject a priori knowledge (e.g. cultural values), and (3) associated metrics of predictive capabilities (e.g. cultural networks). In a simple scenario for target identification with deception, cultural information impacts on situational understanding is demonstrated using the Technology-Emotion-Culture-Knowledge (TECK) attributes of the Observe-Orient-Decide-Act (OODA) model.

A novel approach for vehicle and pedestrian detection based on data fusion techniques is presented. The work fuses information from a 2D laser scanner and a computer camera, to provide detection and classification of vehicles and pedestrians in road environments. Thanks to the data fusion approach, the limitations of each sensor are overcome. Thus reliable system is provided, fulfilling the demands of road safety applications. Classification is performed using each sensor independently. Laser scanner approach is based in pattern matching and vision approach is based in the classical Histogram of Oriented Gradients features approach. A higher stage performs data fusion using Kalman Filter and Global Nearest Neighbors. ; This work was supported by the Spanish Government through the Cicyt projects (GRANT TRA2010-20225-C03-01) and (GRANT TRA 2011-29454-C03-02). CAM through SEGAUTO-II (S2009/DPI-1509).