Dr. Ivan Kadar Profile

Dr. Ivan Kadar

SPIE Involvement:

Conference Chair | Track Chair | Author | Editor

Publications (45)

Proceedings Article | 7 June 2024 Presentation + Paper

Digital twin meets information fusion: panel summary

Erik Blasch, Genshe Chen, Yu Chen, Yufeng Zheng, Lynne Grewe, Ivan Kadar, Andreas Savakis

Proceedings Volume 13057, 130570F (2024) https://doi.org/10.1117/12.3014996

KEYWORDS: Data modeling, Systems modeling, Information fusion, Sensors, Instrument modeling, Design, Data fusion, Medicine, Engineering, Aerospace engineering

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Proceedings Article | 8 June 2022 Presentation + Paper

Joint data learning panel summary

Erik Blasch, Andreas Savakis, Yufeng Zheng, Genshe Chen, Ivan Kadar, Uttam Majumder, Ali Raz

Proceedings Volume 12122, 121220K (2022) https://doi.org/10.1117/12.2619537

KEYWORDS: Synthetic aperture radar, Data modeling, Data fusion, Systems engineering, Information fusion, Systems modeling, Sensors, Image fusion, Target detection, Social networks

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Proceedings Article | 14 April 2021 Presentation

Invited panel discussion: joint data learning

Lynne Grewe, Chee-Yee Chong, Ivan Kadar

Proceedings Volume 11756, 1175602 (2021) https://doi.org/10.1117/12.2598863

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Proceedings Article | 26 May 2020 Presentation + Paper

Machine learning in/with information fusion for infrastructure understanding, panel summary

Erik Blasch, Lynne Grewe, Edward Waltz, Paul Bendich, Vladimir Pavlovic, Ivan Kadar, Chee-Yee Chong

Proceedings Volume 11423, 114230O (2020) https://doi.org/10.1117/12.2559416

KEYWORDS: Data modeling, Information fusion, Machine learning, Data fusion, Sensors, Systems modeling, Telecommunications, Artificial intelligence, Video, Image fusion

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Proceedings Article | 16 May 2019 Paper

A framework for context change detection and management in probabilistic models for context in fusion

Ivan Kadar, Chee-Yee Chong, Robert Schutz

Proceedings Volume 11018, 110180P (2019) https://doi.org/10.1117/12.2520529

KEYWORDS: Data modeling, Systems modeling, Sensors, Detection and tracking algorithms, Performance modeling, Data fusion, Information fusion, Analytics, Data processing, Machine learning

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Proceedings Article | 7 May 2019 Paper

Deep learning in AI and information fusion panel discussion

Erik Blasch, Ivan Kadar, Lynne Grewe, Garrett Stevenson, Uttam Majumder, Chee-Yee Chong

Proceedings Volume 11018, 110180Q (2019) https://doi.org/10.1117/12.2519230

KEYWORDS: Artificial intelligence, Information fusion, Data modeling, Systems modeling, Sensors, Data fusion, Algorithm development, Evolutionary algorithms, Detection and tracking algorithms, Neural networks

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Proceedings Article | 5 October 2018 Presentation

Impact of emerging quantum information technologies (QIT) on information fusion: panel summary (Conference Presentation)

Erik Blasch, Bhashyam Balaji, Ivan Kadar

Proceedings Volume 10646, 106460R (2018) https://doi.org/10.1117/12.2305578

KEYWORDS: Information fusion, Quantum information, Information technology, Quantum computing, Quantum physics, Sensor technology, Computer science, Quantum communications, Atomic, molecular, and optical physics, Physics

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Proceedings Article | 27 April 2018 Paper

A framework for adaptive MaxEnt modeling within distributed sensors and decision fusion for robust target detection/recognition

Ivan Kadar

Proceedings Volume 10646, 106460M (2018) https://doi.org/10.1117/12.2306146

KEYWORDS: Sensors, Target detection, Binary data, Infrared sensors, Data modeling, Signal detection, Detection and tracking algorithms, Sensor fusion, Computer simulations, Feature extraction

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Proceedings Article | 2 May 2017 Presentation + Paper

Panel summary of cyber-physical systems (CPS) and Internet of Things (IoT) opportunities with information fusion

Erik Blasch, Ivan Kadar, Lynne Grewe, Richard Brooks, Wei Yu, Andres Kwasinski, Stelios Thomopoulos, John Salerno, Hairong Qi

Proceedings Volume 10200, 102000O (2017) https://doi.org/10.1117/12.2264683

KEYWORDS: Information fusion, Sensing systems, Telecommunications, Internet, Computer security, Sensors, Data modeling, Data fusion, Control systems, Navigation systems, Computing systems, Distributed computing

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Proceedings Article | 17 May 2016 Paper

An integrated model of hard and soft context in sensor management

Kenneth Hintz, Ivan Kadar

Proceedings Volume 9842, 98420M (2016) https://doi.org/10.1117/12.2228745

KEYWORDS: Environmental sensing, Sensors, Integrated modeling, Information fusion, Data acquisition, Data fusion, Computing systems, Target acquisition, Systems modeling, Data hiding

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Proceedings Article | 17 May 2016 Paper

Issues and challenges of the applications of context to enhance information fusion: panel summary

Erik Blasch, Ivan Kadar, Chee Chong, Alan Steinberg, Ronald P. Mahler, Shanchieh Jay Yang, Laurie Fenstermacher, Alex Chan, Paul Tandy

Proceedings Volume 9842, 98420L (2016) https://doi.org/10.1117/12.2224029

KEYWORDS: Target detection, Information fusion, Data modeling, Sensors, Data fusion, Image fusion, Analytical research, Process modeling, Performance modeling, Detection and tracking algorithms

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Proceedings Article | 21 May 2015 Paper

Issues and challenges of information fusion in contested environments: panel results

Erik Blasch, Ivan Kadar, Chee Chong, Eric Jones, Jorge Tierno, Laurie Fenstermacher, John Gorman, Georgiy Levchuk

Proceedings Volume 9474, 94740Q (2015) https://doi.org/10.1117/12.2177523

KEYWORDS: Sensors, Information fusion, Data fusion, Intelligence systems, Data modeling, Web 2.0 technologies, Sensor networks, Defense and security, Analytics, Data communications

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Proceedings Article | 20 June 2014 Paper

Summary of tracking and identification methods

Erik Blasch, Chun Yang, Ivan Kadar

Proceedings Volume 9091, 909104 (2014) https://doi.org/10.1117/12.2050260

KEYWORDS: Sensors, Data modeling, Data processing, Target detection, Kinematics, Pattern recognition, Target recognition, Radar, Classification systems, Image classification

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Proceedings Article | 23 May 2013 Paper

Influence versus intent for predictive analytics in situation awareness

Biru Cui, Shanchieh Yang, Ivan Kadar

Proceedings Volume 8745, 87451O (2013) https://doi.org/10.1117/12.2020180

KEYWORDS: Analytics, Social networks, Algorithm development, Data modeling, Social network analysis, Social sciences, Integration, Databases, Information fusion, Machine learning

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Predictive analytics in situation awareness requires an element to comprehend and anticipate potential adversary activities that might occur in the future. Most work in high level fusion or predictive analytics utilizes machine learning, pattern mining, Bayesian inference, and decision tree techniques to predict future actions or states. The emergence of social computing in broader contexts has drawn interests in bringing the hypotheses and techniques from social theory to algorithmic and computational settings for predictive analytics. This paper aims at answering the question on how influence and attitude (some interpreted such as intent) of adversarial actors can be formulated and computed algorithmically, as a higher level fusion process to provide predictions of future actions. The challenges in this interdisciplinary endeavor include drawing existing understanding of influence and attitude in both social science and computing fields, as well as the mathematical and computational formulation for the specific context of situation to be analyzed. The study of ‘influence’ has resurfaced in recent years due to the emergence of social networks in the virtualized cyber world. Theoretical analysis and techniques developed in this area are discussed in this paper in the context of predictive analysis. Meanwhile, the notion of intent, or ‘attitude’ using social theory terminologies, is a relatively uncharted area in the computing field. Note that a key objective of predictive analytics is to identify impending/planned attacks so their ‘impact’ and ‘threat’ can be prevented. In this spirit, indirect and direct observables are drawn and derived to infer the influence network and attitude to predict future threats. This work proposes an integrated framework that jointly assesses adversarial actors’ influence network and their attitudes as a function of past actions and action outcomes. A preliminary set of algorithms are developed and tested using the Global Terrorism Database (GTD). Our results reveals the benefits to perform joint predictive analytics with both attitude and influence. At the same time, we discover significant challenges in deriving influence and attitude from indirect observables for diverse adversarial behavior. These observations warrant further investigation of optimal use of influence and attitude for predictive analytics, as well as the potential inclusion of other environmental or capability elements for the actors.

Proceedings Article | 23 May 2013 Paper

Summary of human social, cultural, behavioral (HSCB) modeling for information fusion panel discussion

Erik Blasch, John Salerno, Ivan Kadar, Shanchieh Yang, Laurie Fenstermacher, Mica Endsley, Lynne Grewe

Proceedings Volume 8745, 87451I (2013) https://doi.org/10.1117/12.2016305

KEYWORDS: Data modeling, Information fusion, Cognitive modeling, Systems modeling, Data fusion, Social networks, Sensors, Tin, Process modeling, Computer simulations

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Proceedings Article | 18 May 2012 Paper

Overview of design of experiments performance assessment of multitarget tracking algorithms

Erik Blasch, Chun Yang, Ivan Kadar, Genshe Chen, Li Bai

Proceedings Volume 8392, 839207 (2012) https://doi.org/10.1117/12.919031

KEYWORDS: Sensors, Detection and tracking algorithms, Data modeling, Performance modeling, Electro optical modeling, Diffractive optical elements, Environmental sensing, Analytical research, Information fusion, Target detection

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Proceedings Article | 5 May 2011 Paper

Comparison of information theoretic divergences for sensor management

Chun Yang, Ivan Kadar, Erik Blasch, Michael Bakich

Proceedings Volume 8050, 80500C (2011) https://doi.org/10.1117/12.883745

KEYWORDS: Sensors, Probability theory, Target detection, Information theory, Target recognition, Error analysis, Distance measurement, Switches, Sensing systems, Control systems

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Proceedings Article | 28 April 2010 Paper

Game theoretic sensor management for target tracking

Dan Shen, Genshe Chen, Erik Blasch, Khanh Pham, Philip Douville, Chun Yang, Ivan Kadar

Proceedings Volume 7697, 76970C (2010) https://doi.org/10.1117/12.850870

KEYWORDS: Sensors, Sensor networks, Detection and tracking algorithms, Telecommunications, Nickel, Switches, Sensor fusion, Environmental sensing, Computing systems, Sensing systems

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Proceedings Article | 11 May 2009 Paper

Multi-sensor management for data fusion in target tracking

Xiaokun Li, Genshe Chen, Erik Blasch, Jim Patrick, Chun Yang, Ivan Kadar

Proceedings Volume 7336, 73360Y (2009) https://doi.org/10.1117/12.819465

KEYWORDS: Sensors, Lawrencium, Data fusion, Control systems, Detection and tracking algorithms, Telecommunications, Surveillance, Environmental sensing, Wireless communications, Target detection

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Proceedings Article | 11 May 2009 Paper

Post-update compensation (PUC) with maneuver indicator

Chun Yang, Erik Blasch, Ivan Kadar

Proceedings Volume 7336, 733606 (2009) https://doi.org/10.1117/12.818661

KEYWORDS: Error analysis, Filtering (signal processing), Electronic filtering, Sensors, Motion models, Statistical analysis, Motion measurement, Target detection, Motion estimation, Digital filtering

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Proceedings Article | 6 May 2008 Paper

Results from levels 2/3 fusion implementations: issues, challenges, retrospectives, and perspectives for the future an annotated perspective

Ivan Kadar, Eloi Bosse, John Salerno, Dale Lambert, Subrata Das, Enrique Ruspini, Bradley Rhodes, Joachim Biermann

Proceedings Volume 6968, 696812 (2008) https://doi.org/10.1117/12.782740

KEYWORDS: Data modeling, Systems modeling, Information fusion, Data fusion, Sensors, Data processing, Cognitive modeling, Analytical research, Process modeling, Mathematical modeling

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Proceedings Article | 16 May 2007 Paper

Invited panel discussion: Issues and challenges in uncertainty representation and management with applications to real-world problems

Ivan Kadar, Enrique Ruspini, Anne-Laure Jousselme, Patrick Maupin, Ronald Mahler, Joseph S. Peri, Pierre Valin, D. Akselrod, A. Sinha, T. Kirubarajan

Proceedings Volume 6567, 65670N (2007) https://doi.org/10.1117/12.741364

KEYWORDS: Probability theory, Data fusion, Sensors, Data modeling, Defense and security, Information fusion, Distributed computing, Chemical elements, Systems modeling, Fuzzy logic

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Proceedings Article | 15 May 2007 Paper

Issues and challenges in resource management and its interaction with levels 2/3 fusion with applications to real-world problems: an annotated perspective

Erik Blasch, Ivan Kadar, Kenneth Hintz, Joachim Biermann, Chee-Yee Chong, John Salerno, Subrata Das

Proceedings Volume 6567, 656712 (2007) https://doi.org/10.1117/12.719613

KEYWORDS: Sensors, Information fusion, Weapons, Data fusion, Data processing, Data acquisition, Target detection, Performance modeling, Inspection, Optimization (mathematics)

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Proceedings Article | 25 May 2006 Paper

Issues in resource management with applications to real-world problems

David Castañón, Ron Mahler, Kenneth Hintz, Jim Reich, Ivan Kadar, M. Farooq, T. Kirubarajan, R. Tharmarasa, T. Sathyan, A. Sinha

Proceedings Volume 6235, 62351O (2006) https://doi.org/10.1117/12.694899

KEYWORDS: Sensors, Data fusion, Sensor networks, Cameras, Sensing systems, Algorithm development, Computing systems, Detection and tracking algorithms, Information fusion, Error analysis

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Proceedings Article | 17 May 2006 Paper

Issues and challenges of knowledge representation and reasoning methods in situation assessment (Level 2 Fusion)

Erik Blasch, Ivan Kadar, John Salerno, Mieczyslaw Kokar, Subrata Das, Gerald Powell, Daniel Corkill, Enrique Ruspini

Proceedings Volume 6235, 623510 (2006) https://doi.org/10.1117/12.669779

KEYWORDS: Data modeling, Process modeling, Systems modeling, Information fusion, Sensors, Knowledge discovery, Error analysis, Data fusion, Cognitive modeling, Process control

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Proceedings Article | 25 May 2005 Paper

Distributed multisensor fusion with network connection management

Ivan Kadar

Proceedings Volume 5809, (2005) https://doi.org/10.1117/12.606997

KEYWORDS: Signal processing, Sensor fusion, Target recognition, Telecommunications, Control systems, Information theory, Sensor networks, Information fusion, System integration, Data communications

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Proceedings Article | 9 August 2004 Paper

Figures-of-merit to bridge fusion, long-term prediction, and dynamic sensor management

Ivan Kadar, KuoChu Chang, Kristin O'Connor, Martin Liggins

Proceedings Volume 5429, (2004) https://doi.org/10.1117/12.544342

KEYWORDS: Sensors, Bridges, Kinematics, Lab on a chip, Performance modeling, Classification systems, Signal processing, Sensor fusion, Target recognition, Motion estimation

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Proceedings Article | 25 August 2003 Paper

Multisource report-level simulator for fusion research

Mark Carlotto, Ivan Kadar

Proceedings Volume 5096, (2003) https://doi.org/10.1117/12.488672

KEYWORDS: Sensors, Unattended ground sensors, Video, Signals intelligence, Data modeling, Mathematical modeling, Radar, Palladium, Error analysis, Systems modeling

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Proceedings Article | 31 July 2002 Paper

Perceptual reasoning in adaptive fusion processing

Ivan Kadar

Proceedings Volume 4729, (2002) https://doi.org/10.1117/12.477619

KEYWORDS: Data fusion, Process modeling, Data modeling, Systems modeling, Sensors, Control systems, Databases, Kinematics, Data processing, Information fusion

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Proceedings Article | 27 September 2001 Paper

Self-organizing cooperative sensor network for remote surveillance: target tracking while optimizing the geometry between bearing-reporting sensors and the target

Richard Burne, Ivan Kadar, Anna Buczak

Proceedings Volume 4393, (2001) https://doi.org/10.1117/12.441266

KEYWORDS: Sensors, Surveillance, Error analysis, Acoustics, Unattended ground sensors, Sensor networks, Roads, Magnetic sensors, Optical inspection, Detection and tracking algorithms

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Proceedings Article | 16 August 2001 Paper

Perceptual reasoning managed situation assessment and adaptive fusion processing

Ivan Kadar

Proceedings Volume 4380, (2001) https://doi.org/10.1117/12.436965

KEYWORDS: Process modeling, Systems modeling, Sensors, Data fusion, Content addressable memory, Data modeling, Control systems, Phase modulation, Kinematics, Network architectures

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Proceedings Article | 21 February 2001 Paper

Self-organizing cooperative sensor network for remote surveillance: improved target tracking results

Richard Burne, Ivan Kadar, John Whitson, Anna Buczak

Proceedings Volume 4232, (2001) https://doi.org/10.1117/12.417546

KEYWORDS: Sensors, Surveillance, Acoustics, Unattended ground sensors, Kinematics, Sensor networks, Detection and tracking algorithms, Target recognition, Taxonomy, Magnetic sensors

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Proceedings Article | 4 August 2000 Paper

Optimum connection management scheduling

Ivan Kadar

Proceedings Volume 4052, (2000) https://doi.org/10.1117/12.395094

KEYWORDS: Sensors, Satellites, Data fusion, Telecommunications, Sensor networks, Information fusion, Data communications, Sensor fusion, Composites, Databases

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Proceedings Article | 21 July 2000 Paper

Self-organizing cooperative UGS network for target tracking

Richard Burne, Ivan Kadar, John Whitson, Eitan Eadan

Proceedings Volume 4040, (2000) https://doi.org/10.1117/12.392565

KEYWORDS: Sensors, Acoustics, Unattended ground sensors, Surveillance, Detection and tracking algorithms, Error analysis, Sensor networks, Target detection, Roads, Magnetic sensors

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Proceedings Article | 30 July 1999 Paper

Self-organizing cooperative sensor network for remote surveillance: current results

Richard Burne, Anna Buczak, Yaochu Jin, Vikram Jamalabad, Ivan Kadar, Eitan Eadan

Proceedings Volume 3713, (1999) https://doi.org/10.1117/12.357139

KEYWORDS: Sensors, Surveillance, Target detection, Acoustics, Unattended ground sensors, Error analysis, Sensor networks, Roads, Detection and tracking algorithms, Algorithm development

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Proceedings Article | 7 January 1999 Paper

Self-organizing cooperative sensor network for remote surveillance

Richard Burne, Anna Buczak, Vikram Jamalabad, Ivan Kadar, Eitan Eadan

Proceedings Volume 3577, (1999) https://doi.org/10.1117/12.336957

KEYWORDS: Sensors, Target detection, Surveillance, Unattended ground sensors, Sensor networks, Optimization (mathematics), Relays, Detection and tracking algorithms, Genetic algorithms, Acoustics

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The capabilities of unattended ground sensors (UBSs) have steadily improved and have been shown to be of value in various military missions. Today's UGS are multifunctional, integrated sensor platforms that can detect and locate a wide variety of ground-based and airborne targets. Due primarily to cost and size constraints of these UGS, they have not been widely used for law enforcement surveillance applications. As an alternative to a single, monolithic sensor platform, remote surveillance may be possible with smaller, less obtrusive sensors that work cooperatively together as a network. The objective of this study was to develop algorithms that can optimally organize and adaptively control a network of UGSs in order to achieve a surveillance mission. In the present study, the sensor network, a random distribution of sensors over a surveillance area (emulates airborne sensor deployment), determines an optimal combination of its sensors that will detect multiple targets and consume the lease amount of power. This problem is considered a multiobjective optimization problem to which there is no unique solution. Furthermore, for a linearly increasing number of sensors, the combinatorial search space increases exponentially. To reduce the search space, a novel clustering method was developed based on whether the sensor can sense the target rather than on similarities between the sensors. A genetic algorithm (GA) was used to obtain a quasioptimal solution for the sensor combination problem. To evaluate the effectiveness of the optimization, figures of merit were developed that are applicable to a sensor network tasked with a surveillance problem. Software-simulated data was used to test software implementation of the clustering, optimization and figure of merit functions. The clustering method reduced the search space by an average of ten orders of magnitude. For a sensor population of 100 sensors that was tasked to detect 24 targets, the GA was able to select optimal sets of sensors for detection and minimization of power consumption. The results demonstrate the feasibility of optimally configuring and controlling a network of sensors for remote surveillance applications.

Proceedings Article | 3 September 1998 Paper

Ground-target tracking with topography-based variable-structure IMM estimator

Thiagalingam Kirubarajan, Yaakov Bar-Shalom, Krishna Pattipati, Ivan Kadar

Proceedings Volume 3373, (1998) https://doi.org/10.1117/12.324622

KEYWORDS: Roads, Motion models, Motion estimation, Detection and tracking algorithms, Sensors, Error analysis, Data modeling, Visibility, Process modeling, Target detection

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Proceedings Article | 17 July 1998 Paper

Optimum geometry selection for sensor fusion

Ivan Kadar

Proceedings Volume 3374, (1998) https://doi.org/10.1117/12.327141

KEYWORDS: Sensors, Error analysis, Sensor fusion, 3D acquisition, Precision measurement, Ranging, Model-based design, Systems modeling, Computing systems, Mathematical modeling

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Proceedings Article | 28 July 1997 Paper

Comparison of robustized assignment algorithms

Ivan Kadar, Eitan Eadan, Richard Gassner

Proceedings Volume 3068, (1997) https://doi.org/10.1117/12.280802

KEYWORDS: Matrices, Chemical elements, Detection and tracking algorithms, Distance measurement, Human-machine interfaces, Computing systems, Sensor fusion, Distributed computing, Monte Carlo methods, Data storage

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Proceedings Article | 14 June 1996 Paper

Robust adaptive signal-level fusion for enhanced target detection

Ivan Kadar

Proceedings Volume 2755, (1996) https://doi.org/10.1117/12.243165

KEYWORDS: Algorithm development, Signal detection, Target detection, Detection and tracking algorithms, Radar, Receivers

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Proceedings Article | 5 July 1995 Paper

Adaptive sensor fusion

Ivan Kadar

Proceedings Volume 2484, (1995) https://doi.org/10.1117/12.213016

KEYWORDS: Sensors, Control systems, Process modeling, Data modeling, Sensor fusion, Surveillance systems, Data processing, Phase modulation, Data fusion, Sensor performance

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Proceedings Article | 10 June 1994 Paper

Robust centralized predetection fusion for enhanced target detection

Ivan Kadar, Bernard Abrams, Louis Lovas, Mark Alford, William Berry, Martin Liggins

Proceedings Volume 2232, (1994) https://doi.org/10.1117/12.177766

KEYWORDS: Target detection, Data fusion, Receivers, Sensors, Monte Carlo methods

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Proceedings Article | 1 February 1994 Paper

Predetection fusion for enhanced surveillance

Ivan Kadar, Stelios Thomopoulos, James Llinas, Mark Alford, Martin Liggins

Proceedings Volume 2093, (1994) https://doi.org/10.1117/12.172529

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Proceedings Article | 3 September 1993 Paper

Multispectral data fusion using neural networks

Richard Haberstroh, Ivan Kadar

Proceedings Volume 1955, (1993) https://doi.org/10.1117/12.155002

KEYWORDS: Data modeling, Signal to noise ratio, Neural networks, Digital filtering, Monte Carlo methods, Modulation, Feature extraction, Data fusion, Algorithm development, Surveillance

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Proceedings Article | 3 September 1993 Paper

Design of a multisensor data fusion system for target detection

Stelios Thomopoulos, Nickens Okello, Ivan Kadar, Louis Lovas

Proceedings Volume 1955, (1993) https://doi.org/10.1117/12.154967

KEYWORDS: Sensors, Data fusion, Radar, Data modeling, Target detection, Signal detection, Signal processing, Signal to noise ratio, Statistical modeling, Interference (communication)

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Showing 5 of 45 publications

Proceedings Volume Editor (32)

SPIE Conference Volume | 20 July 2023

Signal Processing, Sensor/Information Fusion, and Target Recognition XXXII

SPIE Conference Volume | 6 July 2022

Signal Processing, Sensor/Information Fusion, and Target Recognition XXXI

SPIE Conference Volume | 24 May 2021

Signal Processing, Sensor/Information Fusion, and Target Recognition XXX

SPIE Conference Volume | 5 June 2020

Signal Processing, Sensor/Information Fusion, and Target Recognition XXIX

SPIE Conference Volume | 22 August 2019

Signal Processing, Sensor/Information Fusion, and Target Recognition XXVIII

Showing 5 of 32 publications

Conference Committee Involvement (72)

Signal Processing, Sensor/Information Fusion, and Target Recognition XXXIV

13 April 2025 | Orlando, Florida, United States

Signal Processing, Sensor/Information Fusion, and Target Recognition XXXIII

22 April 2024 | National Harbor, Maryland, United States

Signal Processing, Sensor/Information Fusion, and Target Recognition XXXII

1 May 2023 | Orlando, Florida, United States

Signal Processing, Sensor/Information Fusion, and Target Recognition XXXI

4 April 2022 | Orlando, Florida, United States

Signal Processing, Sensor/Information Fusion, and Target Recognition XXX

12 April 2021 | Online Only, Florida, United States

Signal Processing, Sensor/Information Fusion, and Target Recognition XXIX

27 April 2020 | Online Only, California, United States

Signal Processing, Sensor/Information Fusion, and Target Recognition XXVIII

15 April 2019 | Baltimore, MD, United States

Signal Processing, Sensor/Information Fusion, and Target Recognition XXVII

16 April 2018 | Orlando, FL, United States

Signal Processing, Sensor/Information Fusion, and Target Recognition XXVI

10 April 2017 | Anaheim, CA, United States

Sensors, and Command, Control, Communications, and Intelligence (C3I) Technologies for Homeland Security, Defense, and Law Enforcement Applications XVI

10 April 2017 | Anaheim, CA, United States

Unmanned/Unattended Sensors and Sensor Networks

27 September 2016 | Edinburgh, United Kingdom

Signal Processing, Sensor/Information Fusion, and Target Recognition XXV

18 April 2016 | Baltimore, MD, United States

Sensors, and Command, Control, Communications, and Intelligence (C3I) Technologies for Homeland Security, Defense, and Law Enforcement Applications XV

18 April 2016 | Baltimore, MD, United States

Unmanned/Unattended Sensors and Sensor Networks

23 September 2015 | Toulouse, France

Signal Processing, Sensor/Information Fusion, and Target Recognition XXIV

20 April 2015 | Baltimore, MD, United States

Sensors, and Command, Control, Communications, and Intelligence (C3I) Technologies for Homeland Security, Defense, and Law Enforcement Applications XIV

20 April 2015 | Baltimore, MD, United States

Unmanned/Unattended Sensors and Sensor Networks

24 September 2014 | Amsterdam, Netherlands

Sensors, and Command, Control, Communications, and Intelligence (C3I) Technologies for Homeland Security and Homeland Defense XIII

5 May 2014 | Baltimore, MD, United States

Signal Processing, Sensor/Information Fusion, and Target Recognition XXIII

5 May 2014 | Baltimore, MD, United States

Unmanned/Unattended Sensors and Sensor Networks X

23 September 2013 | Dresden, Germany

Signal Processing, Sensor Fusion, and Target Recognition XXII

29 April 2013 | Baltimore, Maryland, United States

Sensors, and Command, Control, Communications, and Intelligence (C3I) Technologies for Homeland Security and Homeland Defense XII

29 April 2013 | Baltimore, Maryland, United States

Unmanned/Unattended Sensors and Sensor Networks

26 September 2012 | Edinburgh, United Kingdom

Unattended Ground, Sea, and Air Sensor Technologies and Applications XIV

25 April 2012 | Baltimore, Maryland, United States

Signal Processing, Sensor Fusion, and Target Recognition XXI

23 April 2012 | Baltimore, Maryland, United States

Sensors, and Command, Control, Communications, and Intelligence (C3I) Technologies for Homeland Security and Homeland Defense XI

23 April 2012 | Baltimore, Maryland, United States

Unmanned/Unattended Sensors and Sensor Networks

20 September 2011 | Prague, Czech Republic

Unattended Ground, Sea, and Air Sensor Technologies and Applications XIII

28 April 2011 | Orlando, Florida, United States

Sensors, and Command, Control, Communications, and Intelligence (C3I) Technologies for Homeland Security and Homeland Defense X

25 April 2011 | Orlando, Florida, United States

Signal Processing, Sensor Fusion, and Target Recognition XX

25 April 2011 | Orlando, Florida, United States

Unmanned/Unattended Sensors and Sensor Networks

20 September 2010 | Toulouse, France

Unattended Ground, Sea, and Air Sensor Technologies and Applications XII

5 April 2010 | Orlando, Florida, United States

Sensors, and Command, Control, Communications, and Intelligence (C3I) Technologies for Homeland Security and Homeland Defense IX

5 April 2010 | Orlando, Florida, United States

Signal Processing, Sensor Fusion, and Target Recognition XIX

5 April 2010 | Orlando, Florida, United States

Unmanned/Unattended Sensors and Sensor Networks VI

1 September 2009 | Berlin, Germany

Sensors, and Command, Control, Communications, and Intelligence (C3I) Technologies for Homeland Security and Homeland Defense VIII

15 April 2009 | Orlando, Florida, United States

Unattended Ground, Sea, and Air Sensor Technologies and Applications XI

13 April 2009 | Orlando, Florida, United States

Signal Processing, Sensor Fusion, and Target Recognition XVIII

13 April 2009 | Orlando, Florida, United States

Unmanned/Unattended Sensors and Sensor Networks V

16 September 2008 | Cardiff, Wales, United Kingdom

Sensors, and Command, Control, Communications, and Intelligence (C3I) Technologies for Homeland Security and Homeland Defense VII

17 March 2008 | Orlando, Florida, United States

Signal Processing, Sensor Fusion, and Target Recognition XVII

17 March 2008 | Orlando, Florida, United States

Unattended Ground, Sea, and Air Sensor Technologies and Applications X

17 March 2008 | Orlando, Florida, United States

Unmanned/Unattended Sensors and Sensor Networks IV

18 September 2007 | Florence, Italy

Sensors, and Command, Control, Communications, and Intelligence (C3I) Technologies for Homeland Security and Homeland Defense VI

9 April 2007 | Orlando, Florida, United States

Signal Processing, Sensor Fusion, and Target Recognition XVI

9 April 2007 | Orlando, Florida, United States

Unattended Ground, Sea, and Air Sensor Technologies and Applications IX

9 April 2007 | Orlando, Florida, United States

Unmanned/Unattended Sensors and Sensor Networks III

11 September 2006 | Stockholm, Sweden

Unattended Ground, Sea, and Air Sensor Technologies and Applications VIII

17 April 2006 | Orlando (Kissimmee), Florida, United States

Sensors, and Command, Control, Communications, and Intelligence (C3I) Technologies for Homeland Security and Homeland Defense V

17 April 2006 | Orlando (Kissimmee), Florida, United States

Signal Processing, Sensor Fusion, and Target Recognition XV

17 April 2006 | Orlando (Kissimmee), Florida, United States

Unmanned/Unattended Sensors and Sensor Networks II

26 September 2005 | Bruges, Belgium

Sensors, and Command, Control, Communications, and Intelligence (C3I) Technologies for Homeland Security and Homeland Defense IV

28 March 2005 | Orlando, Florida, United States

Unattended Ground Sensor Technologies and Applications VII

28 March 2005 | Orlando, Florida, United States

Signal Processing, Sensor Fusion, and Target Recognition XIV

28 March 2005 | Orlando, Florida, United States

Unmanned/Unattended Sensors and Sensor Networks

25 October 2004 | London, United Kingdom

Sensors, and Command, Control, Communications, and Intelligence (C3I) Technologies for Homeland Security and Homeland Defense III

12 April 2004 | Orlando, Florida, United States

Signal Processing, Sensor Fusion, and Target Recognition XIII

12 April 2004 | Orlando, Florida, United States

Unattended/Unmanned Ground, Ocean, and Air Sensor Technologies and Applications VI

12 April 2004 | Orlando, Florida, United States

Unattended Ground Sensor Technologies and Applications V

21 April 2003 | Orlando, Florida, United States

Signal Processing, Sensor Fusion, and Target Recognition XII

21 April 2003 | Orlando, Florida, United States

Sensors, and Command, Control, Communications, and Intelligence (C3I) Technologies for Homeland Defense and Law Enforcement II

21 April 2003 | Orlando, Florida, United States

Signal Processing, Sensor Fusion, and Target Recognition XI

1 April 2002 | Orlando, FL, United States

Signal Processing, Sensor Fusion, and Target Recognition X

16 April 2001 | Orlando, FL, United States

Signal Processing, Sensor Fusion, and Target Recognition IX

24 April 2000 | Orlando, FL, United States

Signal Processing, Sensor Fusion, and Target Recognition VIII

5 April 1999 | Orlando, FL, United States

Signal Processing, Sensor Fusion, and Target Recognition VII

13 April 1998 | Orlando, FL, United States

Signal Processing, Sensor Fusion, and Target Recognition VI

21 April 1997 | Orlando, FL, United States

Signal Processing, Sensor Fusion, and Target Recognition V

8 April 1996 | Orlando, FL, United States

Signal Processing, Sensor Fusion, and Target Recognition IV

17 April 1995 | Orlando, FL, United States

Signal Processing, Sensor Fusion, and Target Recognition III

4 April 1994 | Orlando, FL, United States

Signal Processing, Sensor Fusion, and Target Recognition II

12 April 1993 | Orlando, FL, United States

Signal Processing, Sensor Fusion, and Target Recognition

21 April 1992 | Orlando, FL, United States

Showing 5 of 72 Conference Committees

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