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.

REPORT DOCUMENTATION PAGE Form Approved OMB No. 0704-0188Public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. AGENCY USE ONLY (Leave blank)2. REPORT DATE February 1998 REPORT TYPE AND DATES COVEREDInterim Report: April 1996 to February 1997 TITLE AND SUBTITLE Foundations for an Empirically Determined Scale of Trust in Automated Systems AUTHOR(S)Jiun-Yin Jian, Ann M. Bisantz, Colin G. Drury, James Llinas PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES)Center AFRL-HE-WP-TR-2000-0102 SUPPLEMENTARY NOTES 12a. DISTRIBUTION AVAILABILITY STATEMENTApproved for public release; distribution is unlimited. 12b. DISTRIBUTION CODE ABSTRACT (Maximum 200 words)One component in the successful use of automated systems is the extent to which people trust the automation to perform effectively. In order to understand the relationship between trust in computerized systems and the use of those systems, we need to be able to effectively measure trust. Although questionnaires regarding trust have been used in prior studies, these questionnaires were theoretically rather than empirically generated and did not distinguish between three potentially different types of trust: human-human trust, human-machine trust, and trust in general. A three-phased experiment, comprising a word elicitation study, a questionnaire study, and a paired comparison study was performed, in order to better understand similarities and differences in the concepts of trust and distrust, and between the different types of trust. Results indicated that trust and distrust can be considered opposites, rather than comprising different concepts. Components of trust, in terms of words related to trust, were similar across the three types of trust. Results obtained from a cluster analysis were used to identify 12 potential factors of trust between people and automated systems. These 12 factors were then used to develop a proposed scale to measure trust in automation.

REPORT DOCUMENTATION PAGE Form Approved OMB No. 0704-0188Public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. AGENCY USE ONLY (Leave blank)2. REPORT DATE February 1998 REPORT TYPE AND DATES COVEREDInterim Report: April 1996 to February 1997 TITLE AND SUBTITLE Foundations for an Empirically Determined Scale of Trust in Automated Systems AUTHOR(S)Jiun-Yin Jian, Ann M. Bisantz, Colin G. Drury, James Llinas PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES)Center AFRL-HE-WP-TR-2000-0102 SUPPLEMENTARY NOTES 12a. DISTRIBUTION AVAILABILITY STATEMENTApproved for public release; distribution is unlimited. 12b. DISTRIBUTION CODE ABSTRACT (Maximum 200 words)One component in the successful use of automated systems is the extent to which people trust the automation to perform effectively. In order to understand the relationship between trust in computerized systems and the use of those systems, we need to be able to effectively measure trust. Although questionnaires regarding trust have been used in prior studies, these questionnaires were theoretically rather than empirically generated and did not distinguish between three potentially different types of trust: human-human trust, human-machine trust, and trust in general. A three-phased experiment, comprising a word elicitation study, a questionnaire study, and a paired comparison study was performed, in order to better understand similarities and differences in the concepts of trust and distrust, and between the different types of trust. Results indicated that trust and distrust can be considered opposites, rather than comprising different concepts. Components of trust, in terms of words related to trust, were similar across the three types of trust. Results obtained from a cluster analysis were used to identify 12 potential factors of trust between people and automated systems. These 12 factors were then used to develop a proposed scale to measure trust in automation.

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This survey aims to provide a comprehensive status of recent and current research on context-based Information Fusion (IF) systems, tracing back the roots of the original thinking behind the development of the concept of ''context''. It shows how its fortune in the distributed computing world eventually per-meated in the world of IF, discussing the current strategies and techniques, and hinting possible future trends. IF processes can represent context at different levels (structural and physical constraints of the scenario, a priori known operational rules between entities and environment, dynamic relationships modelled to interpret the system output, etc.). In addition to the survey, several novel context exploita-tion dynamics and architectural aspects peculiar to the fusion domain are presented and discussed.

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-The goal of the High-Level Information Fusion (HLIF) Panel Discussion is to present contemporary HLIF advances and developments to determine unsolved grand challenges and issues. The discussion will address the issues between low-level (signal processing and object state estimation and characterization) and high-level information fusion (control, situational understanding, and relationships to the environment). Specific areas of interest include modeling (situations, environments), representations (semantic, knowledge, and complex), systems design (scenario-based, user-based, distributedagent) The HLIF panel discussion's goal is to highlight the unsolved problems and concerns to motivate the information fusion community towards systems-level solutions. The panelists' expert perspectives are based on three areas: (1) previous panel discussions and summaries, (2) an integrated list of HLIF challenges, and (3) companion papers presented at the Fusion2010 conference (note we switch to Fusion10 to refer to the conference). Previous Related Panel DiscussionsPanel discussions provide a valuable resource to the community to overview the current techniques and provide areas of concern for future research. Previous Fusion Conference panel discussion papers related to HLIF include knowledge representation (Fusion05) [7], resource management coordination with situation and threat assessment (Fusion06) [8, 9, 10], agent-based design (Fusion07) [11], and HLIF challenges (Fusion08) [12]. Three panel discussions were conducted at Fusion09 without papers: Many of the authors of this Fusion10 HLIF panel coordinated on previous publications, but continual refinement of HLIF contemporary are desired. The panel discussion follows from a day-long event special session. There are most likely other papers at Fusion10 that are related that would validate good questions from the audience to the panelists. Many of the participants to the special session would be encouraged to voice their opinions and questions to the moderated panel.