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Video of Xi'an

Fusion 2017 Plenary Sessions

Since Fusion 2017 is the 20th fusion conference, a full array of celebration activities will take place, including a special program for the 20th anniversary. Accordingly, the plenary speeches will fit well with the 20th anniversary.

We have three Plenary Sessions.

 

TARGET TRACKING AND DATA FUSION:
How to Get the Most Out of Your Sensors (and make a living out of this)

Abstract: This talk discusses the issues related to information extraction and data fusion from multiple sensors, in particular, from radars.

The goal of extracting the maximum possible amount of information from each sensor requires the use of appropriate sensor as well as target models. In these models one has to quantify the corresponding uncertainties.

The issues related to data association and multiple target behavior models are discussed together with some practical algorithms and their implementations for Low Observable targets, with an example of early acquisition of a VLO TBM. The fusion of the information from various sources has to account for their uncertainties as well as the interrelationship -- crosscorrelations -- between the local track uncertainties across sources. The "Track-to-Track Fusion" and "Centralized Fusion" configurations are discussed.

The conventional fusion techiques have been developed for the case where the state vectors used at different sensors are the same. However, the situation of different state vectors used at different - heterogeneous - sensors requires a special treatment.

While the centralized fusion is the best for linear systems with for homogeneous sensors, the recently developed fusion of state estimates from heterogeneous sensors shows that decentralized fusion can be superior to the centralized one. This is illustrated on an example of fusion from an active and and a passive sensor for a maneuvering target, which requires a nonlinear IMM estimator.

The issue of using the Bayesian or the Fisher model for track-to-track fusion is also discussed and it is shown that they yield the same result.

REFERENCES

Y. Bar-Shalom, P. K. Willett and X. Tian, "Tracking and Data Fusion: A Handbook of Algorithms", YBS Publishing, 2011 (available from amazon.com).

T. Yuan, Y. Bar-Shalom and X. Tian, "Heterogeneous Track-to-Track Fusion", J. of Advances in Information Fusion, 6(2):131--149, Dec. 2011.

Presenter: Yaakov Bar-Shalom

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Yaakov Bar-Shalom received the B.S. and M.S. degrees from the Technion in 1963 and 1967 and the Ph.D. degree from Princeton University in 1970, all in EE. From 1970 to 1976 he was with Systems Control, Inc., Palo Alto,California. Currently he is Board of Trustees Distinguished Professor in the Dept. of Electrical and Computer Engineering and Marianne E. Klewin Professor in Engineering at the University of Connecticut. His current research interests are in estimation theory,target tracking and data fusion. He has published over 550 papers and book chapters. He coauthored/edited 8 books, including Tracking and Data Fusion (YBS Publishing, 2011),He has been elected Fellow of IEEE for "contributions to the theory of stochastic systems and of multitarget tracking". He served as Associate Editor of the IEEE Transactions on Automatic Control and Automatica. He was General Chairman of the 1985 ACC. He served as Chairman of the Conference Activities Board of the IEEE CSS and member of its Board of Governors. He served as General Chairman of FUSION 2000, President of ISIF in 2000 and 2002 and Vice President for Publications during 2004-13.

In 1987 he received the IEEE CSS Distinguished Member Award. Since 1995 he is a Distinguished Lecturer of the IEEE AESS. He is corecipient of the M. Barry Carlton Award for the best paper in the IEEE TAESystems in 1995 and 2000. In 2002 he received the J. Mignona Data Fusion Award from the DoD JDL Data Fusion Group. He is a member of the Connecticut Academy of Science and Engineering. In 2008 he was awarded the IEEE Dennis J. Picard Medal for Radar Technologies and Applications, and in 2012 the Connecticut Medal of Technology. He has been listed by academic.research.microsoft (top authors in engineering) as #1 among the researchers in Aerospace Engineering based on the citations of his work. He is the recipient of the 2015 ISIF Award for a Lifetime of Excellence in Information Fusion. This award has been renamed in 2016 as the Yaakov Bar-Shalom Award for a Lifetime of Excellence in Information Fusion.

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Artificial intelligence to data science:
Challenges and opportunities to information fusion

Abstract: In the early 1980¡¯s, artificial intelligence (AI) was viewed as the solution to information fusion problems. In fact, many contributors to the first distributed sensor networks program were AI researchers. However, inadequate computing and AI approaches such as expert systems and heuristic uncertainty reasoning could not address the challenges of information fusion. Thus, important advances in information fusion, and in particular, multi-target tracking, were made with little contribution from AI.

During the long AI winter, researchers addressed the deficiencies of early AI, developing rigorous representation and reasoning techniques for uncertainty, and machine learning approaches. Recently, data science was established as a popular area to exploit the large volumes of data collected by physical sensors and online activities using machine learning and other analytic tools.

Artificial intelligence and data science pose both challenges and opportunities to information fusion. They are challenges because they appear to address the same problems as information fusion, but with more powerful techniques, thus siphoning away both research funding and research talent. However, these challenges can also be opportunities because AI and data science provide new research directions for information fusion. Examples include information fusion with big data, hard and soft data fusion, learning about context, graph techniques for tracking and fusion, and dynamic network analysis. This talk will discuss applications and techniques.

Presenter: Chee-Yee Chong, independent researcher

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Chee Chong received his S.B. S.M. and Ph.D. degrees, all in Electrical Engineering from the Massachusetts Institute of Technology. He was on the electrical engineering faculty of Georgia Institute of Technology until 1980, when he gave up the security of a tenured position to join a Silicon Valley startup that performed research on advanced information and decision systems. He served as fusion technology research lead at this startup and three other companies until he retired in 2013 to perform independent research in areas that interest him.

He has been involved in research and development in tracking, fusion, and resource management for undersea, surface, ground, air, and space targets for over 25 years. In particular, he is known for his work in distributed tracking and fusion. He developed the first fusion rule for optimally combining probabilities and state estimates, and led the development of the first distributed multiple hypothesis tracking algorithms under DARPA¡¯s Distributed Sensor Networks (DSN) program in early 1980¡¯s. He has been involved in all levels of information fusion, including Bayesian multiple hypothesis tracking for general target and sensor models, optimal track fusion and association, close loop tracking with sensor resource management, analytic model to predict association performance, Bayesian networks for situation assessment, hard and soft data fusion, and graph approaches for data association.

He is the co-author of over one hundred conference papers, journal papers and book chapters, and the co-inventor of three U.S. patents, including one on information fusion for cyber security. He is co-editor of the book ¡°Distributed Data Fusion for Network-Centric Operations¡±, which has been translated into Chinese.

He co-founded the International Society of Information Fusion (ISIF), and served as its President in 2004. He was general co-chair for the 12th International Conference on Information Fusion held in Seattle, USA in 2009. He was associate editor for IEEE Transactions on Automatic Control, associate editor for Information Fusion, and is associate editor for Journal of Advances in Information Fusion published by ISIF. He received the ISIF Yaakov Bar-Shalom Award for a Lifetime of Excellence in Information Fusion in 2016.

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Plenary Panel -- Data Fusion: Whither Now and Why

Panelists: Fred Daum*, Alfonso Farina, Simon Godsill, Fredrik Gustafsson, Kathryn Laskey, Nageswara S.V. Rao , Elisa Shahbazian, and Pramod Varshney#
        *: Cannot make it for health reason.
        #: Cancel the trip last minute for an emergency.


Moderator: X. Rong Li

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Fred Daum is an IEEE Fellow, a Principal Fellow at Raytheon, a Distinguished Lecturer for the IEEE and a graduate of Harvard University. Fred was awarded the Tom Phillips prize for technical excellence, in recognition of his ability to make complex radar systems work in the real World. Fred developed, analyzed and tested the real time tracking, waveform scheduling, calibration and discrimination algorithms for essentially all the long range phased array radars built by the USA in the last four decades, including: Cobra Dane, PAVE PAWS, Cobra Judy, BMEWS SSPAR, ROTHR, SBX, THAAD radar, TPY-2, and UEWR. Fred has also worked on many other systems, including: sonar, GPS, air traffic control radars, ship board fire control radar systems, multi-sensor data fusion systems (e.g., JADGE), as well as JLENS and SPY-3 radars. Fred's exact fixed finite dimensional nonlinear filter theory generalizes the Kalman and Bene? filters. Fred¡¯s particle flow nonlinear filter is many orders of magnitude faster than standard particle filters for the same accuracy. He has published nearly one hundred technical papers, and he has given invited lectures at MIT, Harvard, Yale, Caltech, the Technion, Ecole Normale Superieure de Paris, Brown, Georgia Tech., Duke, Univ. of Connecticut, Univ. of Minnesota, Melbourne Univ., Univ. of Toulouse, Univ. of New South Wales, Univ. of Canterbury, Liverpool Univ., Univ. of Illinois at Chicago, Washington Univ. at St Louis, McMaster Univ., Boston Univ., Northeastern University, WPI, Huntsville, Colorado, Arizona at Tucson and Rutgers.

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Alfonso FARINA, LFIEEE, FIET, FREng, Fellow of EURASIP, received the degree in Electronic Engineering from the University of Rome (IT) in 1973. In 1974, he joined Selenia, then Selex ES, where he became Director of the Analysis of Integrated Systems Unit and subsequently Director of Engineering of the Large Business Systems Division. In 2012, he was Senior VP and Chief Technology Officer of the company, reporting directly to the President. From 2013 to 2014, he was senior advisor to the CTO. He retired in October 2014. From 1979 to 1985, he was also professor of ¡°Radar Techniques¡± at the University of Naples (IT). He is the author of more than 600 peer-reviewed technical publications and of books and monographs (published worldwide), some of them also translated in to Russian and Chinese. Some of the most significant awards he¡¯s received include: (2004) Leader of the team that won the First Prize of the first edition of the Finmeccanica Award for Innovation Technology, out of more than 330 submitted projects by the Companies of Finmeccanica Group; (2005) International Fellow of the Royal Academy of Engineering, U.K., and the fellowship was presented to him by HRH Prince Philip, the Duke of Edinburgh; (2010) IEEE Dennis J. Picard Medal for Radar Technologies and Applications for ¡°Continuous, Innovative, Theoretical, and Practical Contributions to Radar Systems and Adaptive Signal Processing Techniques¡±; (2012) Oscar Masi award for the AULOS? ¡°green¡± radar by the Italian Industrial Research Association (AIRI); (2014) IET Achievement Medal for ¡°Outstanding contributions to radar system design, signal, data and image processing, and data fusion¡±. He is a Visiting Professor at University College London (UCL), Dept. Electronic and Electrical Engineering, CTIF (Center for TeleInFrastructures) Industry Advisory Chair, and a Distinguished Lecturer (DL) of IEEE AESS. He is consultant to Leonardo S.p.A. ¡°Land & Naval Defence Electronics Division¡±.

Please, see also: "A CONVERSATION WITH FRIEND: ALFONSO FARINA", interviewer: Fulvio GINI, IEEE AES SYSTEMS MAGAZINE, June 2016, pp. 41-49. IEEE Aerospace and Electronic Systems Society.

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Simon Godsill is Professor of Statistical Signal Processing in the Engineering Department at Cambridge University. He is also a Professorial Fellow and tutor at Corpus Christi College Cambridge. He coordinates an active research group in Signal Inference and its Applications within the Signal Processing and Communications Laboratory at Cambridge, specializing in Bayesian computational methodology, multiple object tracking, audio and music processing, and financial time series modeling. A particular methodological theme over recent years has been the development of novel techniques for optimal Bayesian filtering and smoothing, using Sequential Monte Carlo or Particle Filtering methods. Prof. Godsill has published extensively in journals, books and international conference proceedings, and has given a number of high profile invited and plenary addresses at conferences such as the Valencia conference on Bayesian Statistics, the IEEE Statistical Signal Processing Workshop and the Conference on Bayesian Inference for Stochastic Processes (BISP) and the IEEE Workshop on Machine Learning in Signal Processing. He co-authored a seminal Springer text Digital Audio Restoration with Prof. Peter Rayner in 1998. He was technical chair of the successful IEEE NSSPW workshop in 2006 on sequential and nonlinear filtering methods, and has been on the conference panel for numerous other conferences/workshops. Prof. Godsill has served as Associate Editor for IEEE Tr. Signal Processing and the journal Bayesian Analysis. He was Theme Leader in Tracking and Reasoning over Time for the UK¡¯s Data and Information Fusion Defence Technology Centre (DIF-DTC) and Principal Investigator on many grants funded by the EU, EPSRC, QinetiQ, General Dynamics, MOD, Microsoft UK, Citibank, Mastercard and Google. In 2009-10 he was co-organiser of an 18 month research program in Sequential Monte Carlo Methods at the SAMSI Institute in North Carolina and in 2014 he co-organised a research programme at the Isaac Newton Institute on Sequential Monte Carlo methods. Two of his journal papers recently received Best Paper awards from the IEEE and IET. He is a Director of CEDAR Audio Ltd. (which has received numerous accolades over the years, including a technical Oscar), a company which utilises his research work in the audio area. In 2016 he delivered a plenary address at the FUSION 2016 conference and in 2018 he will be the General Chair of that conference in Cambridge UK.

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Fredrik Gustafsson is professor in Sensor Informatics at Department of Electrical Engineering, Linkoping University, since 2005. He received the M.Sc. degree in electrical engineering 1988 and the Ph.D. degree in Automatic Control, 1992, both from Linkoping University. His research interests are in stochastic signal processing, adaptive filtering and change detection, with applications to communication, vehicular, airborne, and audio systems.

He is a co-founder of the companies NIRA Dynamics (automotive safety, including tire pressure monitoring systems found in more than 30 million cars), Softube (plug-ins for music studios and software solutions found in for instance Marshall and Fender), and Senionlab (indoor navigation for smart phones deployed in more than 30 countries in all six continents).

He has supervised 25 PhD and more than 200 master theses. He is the author of five books, more than 200 conference papers, some 100 journal papers and some 30 patents. His current h-index is 53 (Google Scholar).

He was an associate editor for IEEE Transactions of Signal Processing 2000-2006, IEEE Transactions on Aerospace and Electronic Systems 2010-2012, and EURASIP Journal on Applied Signal Processing 2007-2012. He was awarded the Arnberg prize by the Royal Swedish Academy of Science (KVA) 2004, elected member of the Royal Academy of Engineering Sciences (IVA) 2007, and elevated to IEEE Fellow 2011. He was awarded the Harry Rowe Mimno Award 2011 for the tutorial "Particle Filter Theory and Practice with Positioning Applications", which was published in the AESS Magazine in July 2010, and was co-author of "Smoothed state estimates under abrupt changes using sum-of-norms regularization" that received the Automatica paper prize in 2014.

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Kathryn Blackmond Laskey is Professor in the Systems Engineering and Operations Research Department and Associate Director of the C4I and Cyber Center at George Mason University. She teaches and performs research in information fusion, systems engineering, decision theoretic knowledge representation and reasoning methodology, Bayesian statistics, decision support, and integration of semantic technology with probability. A major focus of her research has been knowledge representation and inference for higher level multi-source fusion to support situation awareness and decision support. She developed multi-entity Bayesian networks (MEBN), a language and logic that extends classical first-order logic to support probability. She was a key contributor to the development of the PR-OWL language for representing uncertainty in OWL ontologies. She co-chaired the W3C¡¯s Uncertainty Reasoning for the World Wide Web Experimental Group (URW3-XG), which investigated aspects of uncertainty that need to be standardized for web-based applications. She is a member of the Board of Directors of the International Society of Information Fusion. She is Board Chair of the Association for Uncertainty in Artificial Intelligence and Secretary of the Washington Metropolitan Area chapter of INCOSE. She was Co-Chair of the Fusion 2015 conference, has organized numerous workshops and conferences, and has served on boards and committees of the National Academy of Sciences.

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Nageswara S.V. Rao is a Corporate Fellow at Oak Ridge National Laboratory. He received PhD in Computer Science with minor in Mathematics from Louisiana State University in 1988, and ME in Computer Science and Automation from Indian Institute of Science, Bangalore in 1984. His research interests are information and multi-sensor fusion, high performance networking, and sensor networks. He is a Fellow of IEEE. He published more than 400 technical conference and journal papers, and received 2014 R&D100 Award for chaotic-map method for diagnosing hybrid supercomputing systems.

He co-organized 1996 Workshop on Information and Decision Fusion with Applications to Engineering Problems.His contributions to information fusion area include estimating/learning fusers from measurements with probabilistic performance guarantees of their superiorityover subsets of component systems. Applications of these methods include combining ultrasonic and infrared sensor measurements for mobile robots, localization-based detection in radiation sensor networks, well-log measurement fusion for methane hydrate explorations, combining codes for embrittlement prediction of light water reactors, and combining effluence and radiation measurements for reactor activity detection.He received 2005 IEEE Technical Achievement Award with citation ¡°For contributions to the design and analysis of information fusion methods with rigorous performance guarantees and efficient implementations applied to a number of practical problems¡±. His projects in information fusion areas have been supported by multiple US federal agencies, including National Science Foundation, Department of Energy, Office of Naval Research, Domestic Nuclear Detection Office, and Defense Advanced Research Projects Agency.

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Dr. Elisa Shahbazian has received her PhD in High Energy Particle Physics from McGill University in 1983. She started her career in Data/Information Fusion in 1987 at Paramax Montreal (later acquired by Lockheed Martin). She has over 30 years of experience in Data/Information Fusion enabled Decision Support, Command and Control Systems and Systems/Software Engineering. Manager of Science/Engineering and Research/Development, with extensive experience within the aerospace, surveillance and reconnaissance industries.

She has both an in-depth and hands-on understanding of industry and academia, enabling her to create strong partnerships leading to development of both state-of-the-art technology solutions as well as highly qualified young resources from academia. She is recognized for her ability to quickly grasp "the big picture" and set the course for the efficient solution. Strategic thinker, innovative, creative, and strong at setting up project teams, ensuring successful achievement of project goals.

Since August 2008 Elisa Shahbazian is a co-founder and owner of OODA Technologies Inc., which is a Montreal based R&D company for decision support systems working mainly in defence and aerospace.

She is the president of the company, performing duties of the company¡¯s executive officer, performs business development, as well as acts as subject matter expert in Data/Information Fusion and Decision support.

Dr. Shahbazian was 2009 Chair and 2002-2014 Member of the Board of Directors and/or Member Executive Committee of International Society for Information Fusion (ISIF). She was member of the Board of Directors (2000-2010) and Research Management Committee (2000-2014) for Mathematics of Information Technology and Complex Systems (MITACS) Network Centre of Excellence (NCE), Canada. She is also an industrial associate member of Centre de recherches math¨¦matiques of Universit¨¦ de Montr¨¦al since 1994.

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Pramod K. Varshney received the B.S. degree in electrical engineering and computer science (with highest honors), and the M.S. and Ph.D. degrees in electrical engineering from the University of Illinois at Urbana-Champaign in 1972, 1974, and 1976 respectively. Since 1976 he has been with Syracuse University, Syracuse, NY where he is currently a Distinguished Professor of Electrical Engineering and Computer Science and the Director of CASE: Center for Advanced Systems and Engineering. His current research interests are in distributed sensor networks and data fusion, detection and estimation theory, wireless communications, physical layer security, image processing, and radar. He has published 6 books, over 250 journal papers and 500 conference papers.? He is the author of Distributed Detection and Data Fusion, published by Springer-Verlag in 1997. Thus far, he has supervised 59 doctoral dissertations.

While at the University of Illinois, Dr. Varshney was a James Scholar, a Bronze Tablet Senior, and a Fellow. He is a member of Tau Beta Pi and is the recipient of the 1981 ASEE Dow Outstanding Young Faculty Award. He was elected an IEEE Fellow in 1997 for his contributions in the area of distributed detection and data fusion. In 2000, he received the Third Millennium Medal from the IEEE and Chancellor's Citation for exceptional academic achievement at Syracuse University. He is the recipient of the IEEE 2012 Judith A. Resnik Award. He was awarded the degree of Doctor of Engineering honoris causa by Drexel University in 2014 and the ECE Distinguished Alumni Award by the University of Illinois in 2015. He served on the founding board of International Society of Information Fusion (ISIF) and as its President during 2001.

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