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law enforcement users will be able to seamlessly and securely communicate over whatever local point of access is the best fit at any specific location, time, and situation. The second major theme dealt with being able to filter, prioritize, and make sense out of all the new data sent over this network. A common concern was the danger of information overload and how to manage and curate information to make it most useful for various areas of law enforcement, ranging from officers in the field to operations centers and public safety answering points. Specific needs in support of these themes included architectural development, developing guidance for agencies on acquiring, managing, and using new technologies, and conducting research and development on a range of technologies related to bringing about the future hybrid networks and information prioritization.

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We describe basic research that uses a causal, uncertainty-sensitive computational model rooted in qualitative social science to fuse disparate pieces of threat information. It is a cognitive model going beyond rational-actor methods. Having such a model has proven useful when information is uncertain, fragmentary, indirect, soft, conflicting, and even deceptive. Inferences from fusion must then account for uncertainties about the model, the credibility of information, and the fusion methods-i.e. we must consider both structural and parametric uncertainties, including uncertainties about the uncertainties. We use a novel combination of (1) probabilistic and parametric methods, (2) alternative models and model structures, and (3) alternative fusion methods that include nonlinear algebraic combination, variants of Bayesian inference, and a new entropy-maximizing approach. Initial results are encouraging and suggest that such an analytically flexible and model-based approach to fusion can simultaneously enrich thinking, enhance threat detection, and reduce harmful false alarms. INTRODUCTION PurposeThis paper illustrates how we have used a computational version of an originally qualitative socialscience model for basic research on heterogeneous information fusion bearing on detection of potential terrorists. The term "heterogeneous" highlights the diverse character of the information being fused-e.g, behavioral observations in an airport, prior-arrest records, and reports from agents of varied quality and reliability. The information is often qualitative, soft, conflicting, and even deceptive. The model assists in using such diverse and fragmentary information to piece together an estimate of the threat of terrorism posed by the individual. With respect to modeling theory, the paper illustrates the potential value of causal social-science models, assuming that they are used with proper respect for both structural and parametric uncertainties. The context is assisting uncertain inference about threat, rather than making point predictions or issuing firm judgments. Such fusion necessarily includes considerable subjectivity and analytic artistry, but it can be given structure and rigor, and it can include extensive and useful uncertainty analysis. Such improved fusion methods could increase the probability of detecting the rare potential terrorist, decrease false alarms, and increase the probability of exonerating individuals who might otherwise be falsely assessed. Future work will determine how much can be achieved. 2586 978-1-4673-9743-8/15/$31.00 ©2015 IEEE

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This document and trademark(s) contained herein are protected by law. This representation of RAND intellectual property is provided for noncommercial use only. Unauthorized posting of this publication online is prohibited. Permission is given to duplicate this document for personal use only, as long as it is unaltered and complete. Permission is required from RAND to reproduce, or reuse in another form, any of its research documents for commercial use. For information on reprint and linking permissions, please visit www.rand.org/pubs/permissions.html.The RAND Corporation is a research organization that develops solutions to public policy challenges to help make communities throughout the world safer and more secure, healthier and more prosperous. RAND is nonprofit, nonpartisan, and committed to the public interest.RAND's publications do not necessarily reflect the opinions of its research clients and sponsors. For more information on this publication, visit www.rand.org/t/RR1200Published by the RAND Corporation, Santa Monica, Calif. © Copyright 2016 RAND CorporationR® is a registered trademark. Cover Image: Fotolia iii PrefaceThis study was sponsored by the Office of Naval Research (ONR). It was initiated by Ivy Estabrooke and continued under Lee Mastrioanni, thrust manager, and Joong Kim, program officer, in ONR's Expeditionary Maneuver Warfare and Combatting Terrorism Department (Code 30). Comments and questions are welcome and should be addressed to Paul K. Davis at pdavis@rand.org.The report documents a basic research project. It is technical in nature and intended for researchers or managers of technical research potentially interested in information fusion for such domains as counterterrorism, law enforcement, and intelligence. Some of the ideas and methods will be of interest to the larger community of researchers involved with information fusion.This research was sponsored by the Office of Naval Research and conducted within the International Security and Defense Policy Center of the RAND National Defense Research Institute, a federally funded research and development center sponsored by the Office of the Secretary of Defense, the Joint Staff, the Unified Combatant Commands, the Navy, the Marine Corps, the defense agencies, and the defense Intelligence Community.For more information on the International Security and Defense Policy Center, see http://www.rand.org/nsrd/ndri/centers/isdp.html or contact the director (contact information is provided on web page). Summary ObjectivesMilitary and other government organizations put substantial effort into detecting and thwarting attacks such as those by suicide bombers or involving improvised explosive devices. Such attacks may be against military or government installations in the United States or abroad, civilian infrastructure, or any of many other targets. An element of thwarting such attacks is observing suspicious individuals over time with such diverse means as cameras, scanners, and other devices; travel records; behavioral observations; and intelligenc...

The panel was structured to reflect four top-level questions: 1. What are the core public safety applications for VA/SF? 2. What are the specific VA/SF tasks needed to carry out those applications? 3. What security, privacy, and civil rights protections are needed? 4. What technology, policy, and educational needs for innovation are most important to address? The panel specified four key business cases for employing VA/SF in public safety, summarized in Figure S.1. The panelists collectively noted that the use of VA/SF to detect crimes and major incidents potentially in progress (accidents, fires) was the highest priority business case. An example comment was that "we want to stop [crime] from happening, not investigate it later." The panel also identified a core set of technical functions for supporting the business cases and needs for core bodies of research on recognizing objects and events in images, video, and other sensor feeds; developing computational infrastructures; and providing a range of security, privacy, and civil rights protections. The body of this report provides detailed lists of common objects and behaviors that VA/SF systems should be able to detect, along with a list of common security, privacy, and civil rights protections that should be integrated into VA/SF implementations. The panel generated 22 high-priority needs for innovation to enhance the effectiveness and security of VA/SF for law enforcement. These 22 needs, combined with discussion about the business cases and enabling research at the workshop, inform creation of an investment roadmap that describes necessary investments and whether they are near-or long-term investments. Table S.1 summarizes the resulting investment roadmap. In general, the panel found that VA/SF were extremely promising technologies for improving public safety. The capability to detect crimes or major incidents was seen as potentially very valuable for society. The panel also said that VA/SF could be of great benefit in investigating crimes and incidents, could provide major time-savers through automatic reporting, and could support performance mon-• There are 22 high-priority needs for innovation to enhance the effectiveness and security of video analytics and sensor fusion (VA/SF) for law enforcement. • VA/SF could be of great benefit in investigating crimes and incidents. • VA/SF could support law enforcement by monitoring officer performance and protecting officers' health and safety. • The risks of VA/SF technologies are significant, with security, privacy, and civil rights protections needed if these technologies are not to be misused or abused. • While VA/SF technologies are indeed promising for supporting public safety, they have a long way to go before reaching their full potential.