# Full text of "DTIC ADA592356: Information Fusion and Cognitive Processing"

## See other formats

Information Fusion and Cognitive Processing Dr. Rabinder N. Madan, Mathematical, Computer and Information Sciences Office of Naval Research September 22, 2010 703-696-4217 12/9/2011 1 Report Documentation Page Form Approved OMB No. 0704-0188 Public reporting burden for the 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. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden, to Washington Headquarters Services, Directorate for Information Operations and Reports, 1215 Jefferson Davis Highway, Suite 1204, Arlington VA 22202-4302. Respondents should be aware that notwithstanding any other provision of law, no person shall be subject to a penalty for failing to comply with a collection of information if it does not display a currently valid OMB control number. 1. REPORT DATE SEP 2010 2. REPORT TYPE N/A 3. DATES COVERED 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 5d. PROJECT NUMBER 5e. TASK NUMBER 5f. WORK UNIT NUMBER 4. TITLE AND SUBTITLE Information Fusion and Cognitive Processing 6. AUTHOR(S) 7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES) 8. PERFORMING ORGANIZATION Mathematical, Computer and Information Sciences Office of Naval report number Research Arlington, VA, USA 9. SPONSORING/MONITORING AGENCY NAME(S) AND ADDRESS (ES) 10. SPONSOR/MONITOR’S ACRONYM(S) 11. SPONSOR/MONITOR’S REPORT NUMBER(S) 12. DISTRIBUTION/AVAILABILITY STATEMENT Approved for public release, distribution unlimited 13. SUPPLEMENTARY NOTES See also ADA560467. Indo-US Science and Technology Round Table Meeting (4th Annual) - Power Energy and Cognitive Science Held in Bangalore, India on September 21-23, 2010. U.S. Government or Federal Purpose Rights License 14. ABSTRACT In sensor fusion one expects that solutions from individual sensors when combined will lead to a solution that outperforms any one of the individual solutions. Though it is known to be true (Condorcet 1786, Democracy Models) for similar sensors, the story is still unfolding in reality and for dissimilar sensors. In the real world there is a myriad of sensors that are performing similar tasks. They were designed to operate individually and their integration is a current after thought and a compulsion, either to arrive at a better solution or at least a nonconflicting solution. This talk will review the seminal contributions of selected researchers and my involvement in the emergence of the field of data, sensor and information fusion and cognitive processes in sensing. 15. SUBJECT TERMS 16. SECURITY CLASSIFICATION OF: a. REPORT unclassified b. ABSTRACT unclassified c. THIS PAGE unclassified 17. LIMITATION OF 18. NUMBER ABSTRACT OF PAGES SAR 26 19a. NAME OF RESPONSIBLE PERSON Standard Form 298 (Rev. 8-98) Prescribed by ANSI Std Z39-18 My Brush with Science ONR People you may know Science and Information Fusion Cognitive Radar and Sensor Fusion Scientists in the Program • Moeness Amin • Y. Bar-Shalom • Leon Chua • Petar Djuric • Dan Fuhrman • S. S. Iyengar • Thomas Kailath • Rudy Kalman • R. L. Kashyap • Qilian Liang • S. K. Mitra • Arye Nehorai • Athina Petropulu • Unnikrishna Pillai • Vincent Poor • P. P. Vaidyanathan • N. Vishwanadham • Xiadong Wang • Peter Willet Information in Sensor Fusion Combining information from two or more sensors Combining information from different modes of single sensor Fuse information from different algorithms Motivations Limits of performance for single sensors can be pushed only in small increments Multisensor integration MAY LEAD TO improved detection and identification with significantly lower false alarms More data - potential for improved information from measurements Multisensor Integration Sensors designed to operate independently: integration is an “after thought” Fusion approaches have been system and function specific Conventional techniques, models assume identical sensor statistics, equal thresholds, high SNRs, and uncorrelated sensor noise fields - example Radar PDI Issues remain in integrating dissimilar sensors collecting data asynchronously and communicating to central processor with different time delays 18 th Century Information Fusion ■ 1786: Condorcet - Democracy Models: Each individual has probability p of making correct decision: What is the probability of democracy making the correct decision ? Democracy Model p: individual probability of making correct decision; n: number of members of democracy P_n: probability of democracy making correct decision? If p > Vi then P_n > p > Vi P_n approaches 1 as n grows p < Vi then P_n < p < Vi P_n approaches 0 as n grows p= 1/2 then P_n = p= 1/2 Informally, democracy will do well if p >1/2 and will do bad if p<l/2 Information Fusion in Twentieth Century 1956, Reliability: Von Neumann showed how to build a reliable system using unreliable components under independent failures. 1962, Pattern Recognition: Chow showed optimal threshold fuser for multiple independent classifiers. 1969, Forecasting: Bates and Granger, “better” forecasts can be made by combining different forecast methods rather than picking one of them Importance of “fusing” rather than picking the “best” has been demonstrated in a number of disparate disciplines Information Fusion in Late Twentieth Century Advances in Computing and Complex Engineering systems posed new challenges: - Distributed Detection: Bayesian methods for object detection using measurements from different detectors - Sensor Fusion: Multiple sensors became essential to many engineering systems - fusion is part of the problem specification - Mixture of Experts: Function and regression estimation can benefit by combining multiple estimators - Multiple Classifiers: There is no single best classifier but “combined” one is better than its components Information Fusion began taking roots as a discipline unto itself: Office of Naval Research sponsored first workshop on Information Fusion in 1996, jointly with National Science Foundation and Department of Energy First Workshop on Information Fusion Office of Naval Research was the lead sponsor, together with National Science Foundation and Department of Energy Brought together scientists from: Engineering, Computer Science, Mathematics, Econometrics, Bioinformatics, Statistics PROCEEDINGS WORKSHOP ON FOUNDATIONS OF Information / Decision Fusion WITH APPLICATIONS TO ENGINEERING PROBLEMS This workshop launched the field of Information Fusion Editors Nageswara Rao Vladimir Protopope&cu Jacob Barhen Gu na Seetharaman August 7-3, Washington, Sponsors DOE ONR NSF Information Fusion Area Today Integral part of newer disciplines including: • Distributed Sensor Networks • Cyber Data Mining • Cognitive sensor Fusion Dedicated International Conferences: 1. International Conference on Information Fusion (13th in Edinburgh, 2010 ) 2. International Conference on Multisensor Fusion and Integration (Salt Lake City, UT, 2010) Journals: Information Fusion (2000) International Journal of Distributed Sensor Networks (2005) Journal of Advances in Information Fusion (2006) Information Fusion area - last decade or two? Rich Information Sources - Sophisticated sensors - visual, hyperspectral, radiation, chemical, biological, and others - Information sensors - web crawlers, information servers, sophisticated databases Advances in Computation - Fusion problems have become complex - Powerful computer hardware and algorithms have been developed Advances in Networking - Made access to computing and data resource easier - Wireless networking made ad hoc deployments possible - High-performance networks made it possible to support large data transport and remote control possible NEW in September 2010 IEEE 2010 INTERNATIONAL CONFERENCE ON MULTISENSOR FUSION AND INTEGRATION FOR INTELLIGENT SYSTEMS (IEEE MFI 2010), SEPTEMBER 5-7, 2010 The theme of IEEE MFI 2010 was Cognitive Sensor Fusion Here the goal of multi-sensor fusion systems is to achieve human-like performance in terms of perception, knowledge extraction, and situation assessment, exploiting symbolic and/or dynamical systems approaches. Cyber-Physical Trade-Offs in Distributed Detection Networks Naoeswara S. Rao . David K.Y. Yau, J .C. Chin, Chirs Y.T Ma Oak Ridge National Laboratory Purdue University Rabinder N. Madan Office of Naval Research 2010 IEEE Conference on Multisensor Fusion and I ntegration September 5-7, 2010, Salt Lake City, UT Research Sponsored by Applied Mathematics Program Office of Advanced Scientific Computing Research U.S. Department of Energy IEEE MFI 2010: Cognitive Sensor Fusion i l Cl BEST PAPER A WARD fr Cyber-Physical Trade-Offs in Distributed Detection Networks Nagi Rao, Jren-Chit Chin, David Yau, Chris Y. T. Ma and Rabinder Madan Presented September 6 t 2010 Edward Grant, Program>0nair, MFI 2010 Thomas C- Henderson, General Chair, MFI 2010 Motivation Detection of Low-level Radiation Sources Task: • Detect the sources based on sensor measurements Several underlying math problems related to detection networks are open. Our work -addresses network-based detection -provides answers using statistical estimation and packing numbers Difficulty of Detecting Low-level Radiation Sources The radiation levels are only slightly above the background levels and may appear to be "normal" background variations • Varied Background : Depends on local natural and man-made sources and may vary from area to area • Probabilistic Measurements : Radiation measurements are inherently random due to underlying physical process - gamma radiation measurements follow Poisson Process Several solutions are based on thresholding sensor measurements Well-Studied Problem : Has been studied for decades using single or co-located sensors: analytical, experimental and - sensor networks offer "newer" solutions but also questions Open Mathematical Question : Q1 I s there a mathematical quantification for a network of sensors to achieve better performance than single-sensor detectors? Detection of Sources amidst Background Noise A Traditional Method for Detection: 1. SPRT to infer detection from measurements at sensors; 2. Fuse the Boolean decisions at fusion center. Specific Question : Are there methods that perform better? Generic Question : Are there classes of detection problems that benefit from “fusing" measurements in place of decisions ? Our Results : Answer is yes to both questions under 1. Lipschitz smoothness conditions - limited shielding conditions 2. Vapnik-Chenvenenkis conditions - discrete intensity drops 3. * SPRT-sequential probability ratio test Detection Using Localization fl * s Proposed Method for Detection : 1. Estimate the source parameters using measurements - A s ;(x S9 y s ) 2. Utilize likelihood ratio test □ ~ at the fusion center ^3 ^ 0,1 9 P h 0 9 A 5 ^;^ m.j < p p r o,v r \. 0’ where a _ p ( a £ 1 S ^5 9 A/ S’> y s > x i‘. sensor Explanation of Results A fixed-threshold SPRT detection method optimizes the detection performance within a certain neighborhood of state-space. - characterized by sets S , S r L t h Localization facilitates the adaptation of the threshold to estimated neighborhood of the state-space albeit with a certain error probability. Our method achieves a trading-off between •error probability of the localization method in estimating the neighborhood and needed SPRT thresholds; and •probability of "uncovered" regions of fixed-threshold SPRT By suitable trade-off one can exceed the performance of the latter. Summary of Results I mproved detection using measurements at fusion center compared to existing decision fusion methods, using robust localization, under: General non-smooth conditions: Separability of probability ratios - complex analysis and less intuitive conditions + valid under complex shielding of radiation sources Smoothness conditions: Lipschitz separable probability ratios; and Lipschitz source intensity + intuitive conditions: "bigger" parameter space is better - valid typically under open-space environments First mathematical proofs for this class of problems to show: i) a network of sensors performs better than single or co-located sensors ii) measurement "fusion" performs better than detection fusion Performance improvement is characterized by the |packing number j single scalar Summary We proposed source detection method using a network of sensors: - utilizes localization followed by SPRT - out-performs : under both smooth and non-smooth conditions any single SPRT method; majority and other fusion methods •For radiation point source detection: - performs better than existing decision fusion methods •Shows cyber-physical trade-off: better detection at higher cyber cost •lowest cost: single sensor with SPRT •intermediate cost: SPRT at sensors and Boolean fusion •highest cyber cost: send measurements and localization-based fusion Cognitive Radar The University of Arizona is developing: - Robust Bayesian channel models for target recognition and surveillance - Algorithms for waveform optimization derived from the Bayesian models Cognitive Radar: - An approach to radar that closes the loop between exploitation (signal processing) and control/opfimi7ation ofthe measurements - Interrogate the radar environment through smart control of the interrogation .p rope rties (I: e' v " beamsteefinig ;" PRF; pul'Se" shape)' "=>" opt] ml zeavai fa bl e 1 1 rrie/en ergy - Adaptive measurement control * Where to go, what to transmit, where to aim, who to cooperate with, ... Inte 110gate/ELbirtmiaie the Channel Compute Waveforms & ManeuveE S igtial Prcxes sing; Update Hypothesis Ens enisle < :< i Feed Understanding Back to Transmitter Priorities and Constraints Additional Knowledge Sources Fixed search pattern: Includes areas where target unlikely Adaptive search pattern: Focus radar on important, but uncertain, areas Use past observations to enhance future measurements THE UNIVERSITY OF ARIZONA, Enor Rale 00 Transmissions) Sample Results: Two Applications Target ID * Characterize targets by transfer function and compute variance over the classes: 2-1 M 2-1 Search & Track Target Parameters 6 Describe target parameter space as a grid of 00 H 0.7 06 06 ■ 04 03 02 0.1 - A measure of entropy vs. frequency parameterized by target classes wf CIUHef - - -■ MI wto Clutter SNR wYCkifler Itt rfe band Gain due to adaptive, optimized waveforms target probabilities Convertthe probabilistic representation into the best beam steering location Probability of detecting a weak target moving through search zone; (500-target test) 'THU m -. CNR im * fMt /kM, i'XR icus -40 -36 -30 -26 -20 -15 Energy per Trarrernisekx’i Adaptive Control makes more efficient use of time/eneravi THE UNIVERSITY OF ARIZONA* Ongoing Work At UA: - Computationally efficient and robust probability update procedures • For example, how do we perform stable updates of the Bayesian probability map when interference/clutter have unknown pdf? - Probability updating procedures for multiple platforms (mapping of target parameters to range/Doppler/angle is unique for each platform) - Adaptive PRF selection for range-Doppler ambiguity mitigation - Practical classification algorithms and waveform constraints (e.g., constant modulus) Elsewhere: - Dr. Dan Fuhrmann (now at Michigan Tech University) • “Active-testing surveillance systems, or playing 20 questions with a radar”; 2003 ASAP workshop - Proposes radar measurement optimization via probabilistic representation (closed loop system!) - Dr. Simon Haykin, McMaster University • “Cognitive Radar: A Way of the Future”, IEEE Signal Processing Magazine, Jan. 2006. - Summarizes philosophy of cognitive radar and feedback from receiver to transmitter • Cognitive Tracking Radar; Cognitive Dynamic Systems ( http://soma.mcmaster.ca/havkin.php ) - Dr. Arye Nehorai, Washington University-St. Louis • Adaptive waveform parameters and radar flight path (Asilomar 2008) • "Adaptive polarized waveform design for target tracking based on sequential Bayesian inference," IEEE Trans, on Signal Processing, Mar. 2008. Signal Processing for Networked Sensing A majority of performance improvements in Sensors, Networking and Communication connectivity are expected to come from conceptualization of new systems, and through innovations in Signal Processing. Leverage developments in Signal Processing techniques to bring about improvements in sensing, target resolution, small target detectability, multi-target tracking, and address hard problems in sensor data fusion, track fusion and communications.