Dr. Kuochu C. Chang Profile

Dr. Kuochu C. Chang

Professor at George Mason Univ

SPIE Involvement:

Conference Program Committee | Author

Publications (58)

Proceedings Article | 3 June 2022 Presentation + Paper

S-KISS: semantic based knowledge and information sharing system for defense training and operations

Qingliang Zhao, Huong Dang, Nichole Sullivan, K. C. Chang, T. Clemons, Genshe Chen, Erik Blasch, Khanh Pham

Proceedings Volume 12121, 1212102 (2022) https://doi.org/10.1117/12.2636148

KEYWORDS: Defense systems, Defense and security, Knowledge management, Warfare, Prototyping, Performance modeling, Oceanography, Coastal modeling

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Proceedings Article | 12 April 2021 Presentation + Paper

Anomaly detection of unstructured big data via semantic analysis and dynamic knowledge graph construction

Qingliang Zhao, Jiaoyue Liu, Nichole Sullivan, Kuochu Chang, John Spina, Erik Blasch, Genshe Chen

Proceedings Volume 11756, 117560N (2021) https://doi.org/10.1117/12.2589047

KEYWORDS: Software development, Data acquisition, Artificial intelligence, Information technology, Data processing

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Proceedings Article | 22 April 2020 Paper

A random finite set formalism for multiple hypothesis tracking

Shozo Mori, Chee-Yee Chong, Kuo-Chu Chang

Proceedings Volume 11423, 114231B (2020) https://doi.org/10.1117/12.2566259

KEYWORDS: Target detection, Sensors, Algorithm development, Chemical elements, Fermium, Frequency modulation, Data modeling, Switches

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Proceedings Article | 13 May 2016 Paper

Analysis of a probability-based SATCOM situational awareness model for parameter estimation

Todd Martin, Kuo-Chu Chang, Xin Tian, Genshe Chen

Proceedings Volume 9838, 98380S (2016) https://doi.org/10.1117/12.2228472

KEYWORDS: Situational awareness sensors, Satellite communications, Performance modeling, Systems modeling, Telecommunications, Mathematical modeling, Signal to noise ratio, Transmitters, Receivers, Data modeling

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Proceedings Article | 22 May 2015 Paper

Multi-entity Bayesian network for the handling of uncertainties in SATCOM links

Xin Tian, Genshe Chen, K. Chang, Todd Martin, Tien Nguyen, Khanh Pham, Erik Blasch

Proceedings Volume 9469, 94690B (2015) https://doi.org/10.1117/12.2190854

KEYWORDS: Modulation, Performance modeling, Analytical research, Satellite communications, Atmospheric modeling, Telecommunications, Network security, Satellites, Data communications, Visualization

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Proceedings Article | 21 May 2015 Paper

Weighted Kullback-Leibler average-based distributed filtering algorithm

Kelin Lu, Kuo-Chu Chang, Rui Zhou

Proceedings Volume 9474, 94740U (2015) https://doi.org/10.1117/12.2177493

KEYWORDS: Sensors, Error analysis, Algorithm development, Detection and tracking algorithms, Data fusion, Sensor networks, Monte Carlo methods, Computer simulations, Electronic filtering, Filtering (signal processing)

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Proceedings Article | 18 May 2012 Paper

Immune allied genetic algorithm for Bayesian network structure learning

Qin Song, Feng Lin, Wei Sun, KC Chang

Proceedings Volume 8392, 839215 (2012) https://doi.org/10.1117/12.920298

KEYWORDS: Genetic algorithms, Genetics, Optimization (mathematics), Detection and tracking algorithms, Sensor fusion, Target recognition, Parallel computing, Gallium, Monte Carlo methods, Roads

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Proceedings Article | 18 May 2012 Paper

Bayesian network structure learning using chaos hybrid genetic algorithm

Jiajie Shen, Feng Lin, Wei Sun, KC Chang

Proceedings Volume 8392, 839216 (2012) https://doi.org/10.1117/12.920302

KEYWORDS: Particles, Chaos, Particle swarm optimization, Genetic algorithms, Evolutionary algorithms, Clouds, Lithium, Algorithms, Parallel computing, Optimization (mathematics)

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Proceedings Article | 18 May 2012 Paper

Fundamentals of distributed estimation and tracking

Chee-Yee Chong, Kuo-Chu Chang, Shozo Mori

Proceedings Volume 8392, 83920Y (2012) https://doi.org/10.1117/12.922930

KEYWORDS: Error analysis, Sensors, Information fusion, Signal processing, Telecommunications, Detection and tracking algorithms, Matrices, Data communications, Sensor fusion, Algorithm development

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Proceedings Article | 3 May 2012 Paper

A decision and utility theory construct for dynamic spectrum access systems

Todd Martin, Kuo-Chu Chang

Proceedings Volume 8405, 84050F (2012) https://doi.org/10.1117/12.920794

KEYWORDS: Probability theory, Systems modeling, Dynamical systems, Wireless communications, Mathematical modeling, Signal to noise ratio, Temperature metrology, Modulation, Telecommunications, Stochastic processes

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Proceedings Article | 12 May 2011 Paper

Study of the most probable explanation in hybrid Bayesian networks

Wei Sun, KC Chang

Proceedings Volume 8050, 80500T (2011) https://doi.org/10.1117/12.884039

KEYWORDS: Systems modeling, Binary data, Process modeling, Signal processing, Sun, Sensor fusion, C4I, Systems engineering, Surgery, Medical diagnostics

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Proceedings Article | 5 May 2011 Paper

Fusion and Gaussian mixture based classifiers for SONAR data

Vikas Kotari, KC Chang

Proceedings Volume 8050, 80500U (2011) https://doi.org/10.1117/12.883697

KEYWORDS: Data fusion, Sensors, Signal detection, Mining, Data communications, Land mines, Naval mines, Sensor fusion, Data conversion, Coastal modeling

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Proceedings Article | 5 May 2011 Paper

Learning Bayesian network structure using a cloud-based adaptive immune genetic algorithm

Qin Song, Feng Lin, Wei Sun, KC Chang

Proceedings Volume 8050, 80500S (2011) https://doi.org/10.1117/12.883013

KEYWORDS: Clouds, Genetic algorithms, Evolutionary algorithms, Expectation maximization algorithms, Stochastic processes, Detection and tracking algorithms, Optimization (mathematics), Monte Carlo methods, Roads, C4I

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Proceedings Article | 28 April 2010 Paper

The convergence analysis of parallel genetic algorithm based on allied strategy

Feng Lin, Wei Sun, K. C. Chang

Proceedings Volume 7697, 76970P (2010) https://doi.org/10.1117/12.852046

KEYWORDS: Genetics, Genetic algorithms, Stochastic processes, Curium, Magnesium, Matrices, Optimization (mathematics), Data fusion, Thulium, Biological research

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Proceedings Article | 28 April 2010 Paper

Constraint optimized weight adaptation for Gaussian mixture reduction

H. D. Chen, K. C. Chang, Chris Smith

Proceedings Volume 7697, 76970N (2010) https://doi.org/10.1117/12.851993

KEYWORDS: Algorithm development, Detection and tracking algorithms, Monte Carlo methods, Information fusion, Computer simulations, Control systems, Aerospace engineering, Optimization (mathematics), Acoustics, Speech recognition

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Proceedings Article | 28 April 2010 Paper

Direct message passing for hybrid Bayesian networks and performance analysis

Wei Sun, K. C. Chang

Proceedings Volume 7697, 76970S (2010) https://doi.org/10.1117/12.852088

KEYWORDS: Monte Carlo methods, Algorithm development, Systems modeling, Error analysis, Network security, Numerical simulations, Sun, Sensor fusion, Complex systems, Analytical research

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Probabilistic inference for hybrid Bayesian networks, which involves both discrete and continuous variables, has been an important research topic over the recent years. This is not only because a number of efficient inference algorithms have been developed and used maturely for simple types of networks such as pure discrete model, but also for the practical needs that continuous variables are inevitable in modeling complex systems. Pearl's message passing algorithm provides a simple framework to compute posterior distribution by propagating messages between nodes and can provides exact answer for polytree models with pure discrete or continuous variables. In addition, applying Pearl's message passing to network with loops usually converges and results in good approximation. However, for hybrid model, there is a need of a general message passing algorithm between different types of variables. In this paper, we develop a method called Direct Message Passing (DMP) for exchanging messages between discrete and continuous variables. Based on Pearl's algorithm, we derive formulae to compute messages for variables in various dependence relationships encoded in conditional probability distributions. Mixture of Gaussian is used to represent continuous messages, with the number of mixture components up to the size of the joint state space of all discrete parents. For polytree Conditional Linear Gaussian (CLG) Bayesian network, DMP has the same computational requirements and can provide exact solution as the one obtained by the Junction Tree (JT) algorithm. However, while JT can only work for the CLG model, DMP can be applied for general nonlinear, non-Gaussian hybrid model to produce approximate solution using unscented transformation and loopy propagation. Furthermore, we can scale the algorithm by restricting the number of mixture components in the messages. Empirically, we found that the approximation errors are relatively small especially for nodes that are far away from the discrete parent nodes. Numerical simulations show encouraging results.

Proceedings Article | 28 April 2010 Paper

Image-based tracking and sensor resource management for UAVs in an urban environment

Ashwin Samant, K. C. Chang

Proceedings Volume 7697, 76970D (2010) https://doi.org/10.1117/12.852100

KEYWORDS: Unmanned aerial vehicles, Detection and tracking algorithms, Monte Carlo methods, Algorithm development, Sensors, Computer simulations, Environmental sensing, Control systems, Environmental monitoring, Optimization (mathematics)

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Proceedings Article | 28 April 2010 Paper

A comparison of distance metrics between mixture distributions

Ashirvad Rameshwar Naik, K. C. Chang

Proceedings Volume 7697, 76970O (2010) https://doi.org/10.1117/12.852043

KEYWORDS: Distance measurement, Signal to noise ratio, Systems modeling, Content based image retrieval, Image retrieval, Target recognition, Sensor fusion, Image classification, Speech recognition, Nonlinear filtering

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Proceedings Article | 12 May 2009 Paper

An epidemic model for biological data fusion in ad hoc sensor networks

K. Chang, Vikas Kotari

Proceedings Volume 7336, 73360P (2009) https://doi.org/10.1117/12.820245

KEYWORDS: Sensors, Data communications, Data fusion, Data centers, Sensor networks, Monte Carlo methods, Computer simulations, Data modeling, Detection and tracking algorithms, Sensor performance

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Proceedings Article | 11 May 2009 Paper

A dynamic path planning algorithm for UAV tracking

Hongda Chen, K. C. Chang, Craig Agate

Proceedings Volume 7336, 73360B (2009) https://doi.org/10.1117/12.819836

KEYWORDS: Unmanned aerial vehicles, Detection and tracking algorithms, Roads, Particles, Control systems, Particle filters, Computer simulations, Cameras, Target detection, Kinematics

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Proceedings Article | 6 May 2009 Paper

Space-based IR tracking bias removal using background star observations

T. Clemons, K. C. Chang

Proceedings Volume 7330, 73300D (2009) https://doi.org/10.1117/12.817514

KEYWORDS: Sensors, Stars, Satellites, Filtering (signal processing), Infrared sensors, Target detection, Missiles, Infrared search and track, Electronic filtering, Infrared imaging

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Proceedings Article | 17 April 2008 Paper

Analytical performance evaluation for autonomous sensor fusion

K. C. Chang

Proceedings Volume 6968, 69680Y (2008) https://doi.org/10.1117/12.779548

KEYWORDS: Information fusion, Error analysis, Monte Carlo methods, Sensors, Network architectures, Sensor fusion, Data fusion, Algorithm development, Distributed computing, Sensor networks

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Proceedings Article | 17 April 2008 Paper

Effect of sensor bias on space-based bearing-only tracker

T. Clemons, K. C. Chang

Proceedings Volume 6968, 696809 (2008) https://doi.org/10.1117/12.780126

KEYWORDS: Sensors, Satellites, Missiles, Infrared sensors, Filtering (signal processing), Error analysis, Infrared imaging, Infrared search and track, Space operations, Stars

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Proceedings Article | 17 April 2008 Paper

Convergence study of message passing in arbitrary continuous Bayesian networks

Wei Sun, K. C. Chang

Proceedings Volume 6968, 69680Z (2008) https://doi.org/10.1117/12.780626

KEYWORDS: Algorithm development, Detection and tracking algorithms, Algorithms, Statistical modeling, Stochastic processes, Sun, Systems engineering, Information fusion, Signal processing, Sensor fusion

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Proceedings Article | 16 April 2008 Paper

Comparison of track-to-track fusion algorithms using video sensors on multiple unmanned aerial vehicles

Mahendra Mallick, K. C. Chang

Proceedings Volume 6969, 69690X (2008) https://doi.org/10.1117/12.781523

KEYWORDS: Video, Unmanned aerial vehicles, Detection and tracking algorithms, Information fusion, Cameras, Video surveillance, Video acceleration, Video processing, Sensors, Monte Carlo methods

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Proceedings Article | 25 September 2007 Paper

Distributed fusion using video sensors on multiple unmanned aerial vehicles

Mahendra Mallick, K. Chang

Proceedings Volume 6699, 66990Y (2007) https://doi.org/10.1117/12.735353

KEYWORDS: Video, Unmanned aerial vehicles, Cameras, Sensors, Detection and tracking algorithms, Video surveillance, Video acceleration, Video processing, Infrared sensors, Monte Carlo methods

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Proceedings Article | 17 May 2006 Paper

Using self-organizing maps to determine observation threshold limit predictions in highly variant data

Christopher Paganoni, K. C. Chang, Michael Robblee

Proceedings Volume 6235, 62351H (2006) https://doi.org/10.1117/12.667802

KEYWORDS: Hydrology, Data modeling, Neurons, Temperature metrology, Systems modeling, Data centers, Visualization, Brain mapping, Sensors, Optical inspection

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Proceedings Article | 17 May 2006 Paper

Algorithm comparison for autonomous distributed fusion

Martin Liggins, K.C. Chang

Proceedings Volume 6235, 62350T (2006) https://doi.org/10.1117/12.668888

KEYWORDS: Filtering (signal processing), Electronic filtering, Sensors, Information fusion, Error analysis, Process modeling, Network architectures, Network centric warfare, Data fusion, Reliability

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Proceedings Article | 17 May 2006 Paper

A nonlinear filtering and predication (NFP) method for maneuvering target tracking

H. Chen, K. C. Chang

Proceedings Volume 6235, 623503 (2006) https://doi.org/10.1117/12.668890

KEYWORDS: Detection and tracking algorithms, Filtering (signal processing), Nonlinear filtering, Target detection, Electronic filtering, Performance modeling, Motion models, Systems modeling, Statistical modeling, Statistical analysis

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Proceedings Article | 2 May 2006 Paper

Estimation filters for missile tracking with airborne laser

T. Clemons, K. Chang

Proceedings Volume 6238, 623804 (2006) https://doi.org/10.1117/12.669140

KEYWORDS: Missiles, Particle filters, Particles, Sensors, Filtering (signal processing), Optical filters, Nonlinear filtering, Electronic filtering, Laser missile defense, Airborne laser technology

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SPIE Journal Paper | 1 July 2005

Efficient inference for hybrid dynamic Bayesian networks

Kuo Chu Chang, Hongda Chen

OE, Vol. 44, Issue 07, 077201, (July 2005) https://doi.org/10.1117/12.10.1117/1.1948127

KEYWORDS: Evolutionary algorithms, Detection and tracking algorithms, Optical engineering, Dynamical systems, Computing systems, Target recognition, Computer simulations, Analytical research, Artificial intelligence, Machine learning

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Proceedings Article | 25 May 2005 Paper

Probabilistic inference using linear Gaussian importance sampling for hybrid Bayesian networks

Wei Sun, K. Chang

Proceedings Volume 5809, (2005) https://doi.org/10.1117/12.606064

KEYWORDS: Stochastic processes, Performance modeling, Statistical analysis, Systems modeling, Logic, Computer simulations, Statistical modeling, Detection and tracking algorithms, Data modeling, Intelligence systems

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Proceedings Article | 25 May 2005 Paper

Particle filter with iterative importance sampling for Bayesian networks inference

K. Chang, Donghai He

Proceedings Volume 5809, (2005) https://doi.org/10.1117/12.606063

KEYWORDS: Particle filters, Detection and tracking algorithms, Computer simulations, Stochastic processes, Systems modeling, Algorithm development, Particles, Protactinium, Artificial intelligence, Intelligence systems

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Proceedings Article | 25 August 2004 Paper

Out-of-sequence track filtering using the decorrelated pseudo-measurement approach

Mahendra Mallick, Steven Schmidt, Lucy Pao, K. C. Chang

Proceedings Volume 5428, (2004) https://doi.org/10.1117/12.542934

KEYWORDS: Sensors, Monte Carlo methods, Detection and tracking algorithms, Data fusion, Data communications, Error analysis, Telecommunications, Computer simulations, Data processing, Solids

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Proceedings Article | 9 August 2004 Paper

Performance modeling for dynamic Bayesnets networks

KuoChu Chang, Wei Sun

Proceedings Volume 5429, (2004) https://doi.org/10.1117/12.543764

KEYWORDS: Particles, Performance modeling, Monte Carlo methods, Testing and analysis, Particle filters, Computer simulations, Algorithm development, Detection and tracking algorithms, Dynamical systems, Mathematical modeling

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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 | 9 August 2004 Paper

Information theoretics in the IMM decision process

Martin Liggins, KuoChu Chang

Proceedings Volume 5429, (2004) https://doi.org/10.1117/12.544815

KEYWORDS: Mahalanobis distance, Systems modeling, Sensors, Performance modeling, Data modeling, Distance measurement, Filtering (signal processing), Target detection, Kinematics, Stochastic processes

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Proceedings Article | 9 August 2004 Paper

Efficient inference algorithms for hybrid dynamic Bayesian networks (HDBN)

KuoChu Chang, Hongda Chen

Proceedings Volume 5429, (2004) https://doi.org/10.1117/12.544060

KEYWORDS: Evolutionary algorithms, Performance modeling, Statistical modeling, Detection and tracking algorithms, Artificial intelligence, Machine learning, Analytical research, Pattern recognition, Filtering (signal processing), Target recognition

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Proceedings Article | 9 August 2004 Paper

Performance modeling for multisensor tracking and classification

KuoChu Chang, Eswar Sivaraman, Martin Liggins

Proceedings Volume 5429, (2004) https://doi.org/10.1117/12.544061

KEYWORDS: Sensors, Performance modeling, Kinematics, Error analysis, Data modeling, Classification systems, Target detection, Data fusion, Detection and tracking algorithms, Systems modeling

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SPIE Journal Paper | 1 June 2004

Nonlinear estimation comparison for ballistic missile tracking

Brian Saulson, Kuo Chu Chang

OE, Vol. 43, Issue 06, (June 2004) https://doi.org/10.1117/12.10.1117/1.1738431

KEYWORDS: Error analysis, Radar, Sensors, Filtering (signal processing), Satellites, Nonlinear filtering, Infrared sensors, Phase measurement, Missiles, Information fusion

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Nonlinear filtering for state estimation has been recently advanced to include more accurate and stable alternatives. The extended Kalman filter (EKF), the first and most widely used approach (applied as early as the late 1960s and developed into the early 1980s), uses potentially unstable derivative-based linearization of nonlinear process and/or measurement dynamics. The unscented Kalman filter (UKF), developed after around 1994, approximates a distribution about the mean using a set of calculated sigma points. The central difference filter (CDF), or divided difference filter (DDF), developed after around 1997, uses divided difference approximations of derivatives based on Stirling's interpolation formula and results in a similar mean, but a different covariance from the EKF and using techniques based on similar principles to those of the UKF. We compare the performance of these three approaches to the coordinate conversion problem within the larger problem of ballistic missile tracking under various sensor configurations, target dynamics, measurement update/sensor communication rates, and measurement noise. The coordinate conversion problem here specifically deals with conversion of spherical measurements to Cartesian estimates and vice versa. The importance of filter stability in some cases is emphasized as the EKF shows possible divergence due to linearization errors and overconfident state covariance, while the UKF shows possibly slow convergence due to overly large state covariance approximations. The CDF demonstrates relatively consistent stability, despite its similarities to the UKF. The requirement that the CDF and UKF expected state covariances be positive definite is demonstrated to be unrealistic in a case involving multisensor fusion, indicating the necessity for the reportedly more robust and efficient square-root implementation. Strategies for taking advantage of the strengths (and avoiding the weaknesses) of each filter are proposed.

Proceedings Article | 25 August 2003 Paper

Performance modeling for multisensor data fusion

Kuo Chu Chang, Ying Song, Martin Liggins

Proceedings Volume 5096, (2003) https://doi.org/10.1117/12.486868

KEYWORDS: Sensors, Performance modeling, Kinematics, Data modeling, Error analysis, Target detection, Data fusion, Control systems, Quality systems, Unattended ground sensors

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Proceedings Article | 25 August 2003 Paper

Comparison of nonlinear estimation for ballistic missile tracking

Brian Saulson, Kuo Chu Chang

Proceedings Volume 5096, (2003) https://doi.org/10.1117/12.486833

KEYWORDS: Error analysis, Filtering (signal processing), Radar, Sensors, Infrared sensors, Nonlinear filtering, Satellites, Electronic filtering, Infrared radiation, Information fusion

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Proceedings Article | 25 August 2003 Paper

Comparing probabilistic inference for mixed Bayesian networks

Kuo Chu Chang, Wei Sun

Proceedings Volume 5096, (2003) https://doi.org/10.1117/12.486863

KEYWORDS: Detection and tracking algorithms, Evolutionary algorithms, Computer simulations, Artificial intelligence, Stochastic processes, Algorithm development, Target recognition, Systems modeling, MATLAB, Data fusion

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Proceedings Article | 25 August 2003 Paper

Information theoretics for improved tracking and fusion performance

Martin Liggins, Mark Nebrich, Kuo Chu Chang

Proceedings Volume 5096, (2003) https://doi.org/10.1117/12.498652

KEYWORDS: Sensors, Information fusion, Error analysis, Quality measurement, Manganese, Surveillance, Signal processing, Sensor fusion, Target recognition, Motion models

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SPIE Journal Paper | 1 June 2003

Multistation data fusion for code-division multiple access wireless communications

Zhi Tian, KuoChu Chang

OE, Vol. 42, Issue 06, (June 2003) https://doi.org/10.1117/12.10.1117/1.1573214

KEYWORDS: Data fusion, Signal attenuation, Signal detection, Signal processing, Error analysis, Switching, Optical engineering, Wireless communications, Code division multiplexing, Telecommunications

Proceedings Article | 20 August 1998 Paper

Stochastic approximation approach to predictive encoding for bandwidth saving in situation awareness

Rajat Saha, Kuo-Chu Chang, K. Lee

Proceedings Volume 3393, (1998) https://doi.org/10.1117/12.317671

KEYWORDS: Stochastic processes, Computer programming, Filtering (signal processing), Monte Carlo methods, Telecommunications, Detection and tracking algorithms, Astatine, Control systems, Computer simulations, Electronic filtering

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SPIE Journal Paper | 1 February 1998

Centralized fusion of remote tracks

Rajat Saha, Kuo-Chu Chang

OE, Vol. 37, Issue 02, (February 1998) https://doi.org/10.1117/12.10.1117/1.601632

KEYWORDS: Sensors, Matrices, Fourier transforms, Chemical elements, Detection and tracking algorithms, Optical engineering, Optical tracking, Filtering (signal processing), Sensor fusion, Composites

SPIE Journal Paper | 1 February 1998

Multimodal pattern recognition by modular neural network

Shulin Yang, Kuo-Chu Chang

OE, Vol. 37, Issue 02, (February 1998) https://doi.org/10.1117/12.10.1117/1.602016

KEYWORDS: Neural networks, Pattern recognition, Automatic target recognition, Optical engineering, Optical pattern recognition, Radar, Databases, Neurons, Network architectures, Fourier transforms

Proceedings Article | 28 July 1997 Paper

Fusion of synchronous tracks

Rajat Saha, Kuo-Chu Chang, Mieczyslaw Kokar

Proceedings Volume 3068, (1997) https://doi.org/10.1117/12.280799

KEYWORDS: Sensors, Radar, Detection and tracking algorithms, 3D modeling, Infrared sensors, Monte Carlo methods, 3D acquisition, Surveillance, Target detection, Filtering (signal processing)

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SPIE Journal Paper | 1 March 1997

Target identification with Bayesian networks in a multiple hypothesis tracking system

Kuo-Chu Chang, Robert Fung

OE, Vol. 36, Issue 03, (March 1997) https://doi.org/10.1117/12.10.1117/1.601266

KEYWORDS: Target detection, Radar, Sensors, Detection and tracking algorithms, Target recognition, Modulation, Algorithm development, Optical engineering, Infrared sensors, Filtering (signal processing)

Proceedings Article | 14 June 1996 Paper

Bayesian probabilistic inference for target recognition

Kuo-Chu Chang, Jun Liu, Jing Zhou

Proceedings Volume 2755, (1996) https://doi.org/10.1117/12.243157

KEYWORDS: Target recognition, Detection and tracking algorithms, Databases, Algorithm development, Radar, Sensors, Weapons, Computer simulations, Data modeling, Stochastic processes

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Proceedings Article | 14 June 1996 Paper

Modular neural net architecture for automatic target recognition

Shulin Yang, Kuo-Chu Chang

Proceedings Volume 2755, (1996) https://doi.org/10.1117/12.243158

KEYWORDS: Neural networks, Automatic target recognition, Pattern recognition, Radar, Synthetic aperture radar, Neurons, Evolutionary algorithms, Fourier transforms, Algorithms, Network architectures

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Proceedings Article | 10 June 1996 Paper

SAR classification and polarimetric fusion

Andrew Hauter, Kuo-Chu Chang, Sherman Karp

Proceedings Volume 2757, (1996) https://doi.org/10.1117/12.242054

KEYWORDS: Sensors, Synthetic aperture radar, Polarimetry, Polarization, Binary data, Error analysis, Detection and tracking algorithms, Data fusion, Image fusion, Automatic target recognition

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Proceedings Article | 24 May 1996 Paper

Synthetic aperture radar hybrid ATR system

Andrew Hauter, Kuo-Chu Chang

Proceedings Volume 2756, (1996) https://doi.org/10.1117/12.241144

KEYWORDS: Wavelets, Synthetic aperture radar, Automatic target recognition, Detection and tracking algorithms, Target recognition, Feature extraction, Crystals, Distance measurement, Polarization, Discrete wavelet transforms

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SPIE Journal Paper | 1 March 1996

Feature-based target recognition with a Bayesian network

Jun Liu, Kuo-Chu Chang

OE, Vol. 35, Issue 03, (March 1996) https://doi.org/10.1117/12.10.1117/1.600638

KEYWORDS: Target recognition, Synthetic aperture radar, Data modeling, Feature selection, Radar, Sensors, Detection and tracking algorithms, Polarimetry, Automatic target recognition, Target detection

Proceedings Article | 5 July 1995 Paper

Neural network approach for high-resolution target classification

Yi-Chuan Lu, Kuo-Chu Chang

Proceedings Volume 2484, (1995) https://doi.org/10.1117/12.213049

KEYWORDS: Distance measurement, Automatic target recognition, Neural networks, Synthetic aperture radar, Detection and tracking algorithms, Classification systems, Target detection, Radar, Neurons, Image resolution

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Proceedings Article | 6 July 1994 Paper

FUSEDAT: a software package for fusion and data association and tracking with multiple sensors

Kuo-Chu Chang, Yaakov Bar-Shalom

Proceedings Volume 2235, (1994) https://doi.org/10.1117/12.179074

KEYWORDS: Sensors, Data fusion, Detection and tracking algorithms, MATLAB, Personal digital assistants, Signal to noise ratio, Error analysis, Prototyping, Target detection, Filtering (signal processing)

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Proceedings Article | 1 February 1994 Paper

Application of Bayes nets in sensor fusion

Kuo-Chu Chang, Clayton Stewart

Proceedings Volume 2093, (1994) https://doi.org/10.1117/12.172543

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Showing 5 of 58 publications

Conference Committee Involvement (22)

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

14 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

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

18 April 2016 | Baltimore, MD, United States

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

20 April 2015 | Baltimore, MD, United States

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

5 May 2014 | Baltimore, MD, United States

Signal Processing, Sensor Fusion, and Target Recognition XXII

29 April 2013 | Baltimore, Maryland, United States

Signal Processing, Sensor Fusion, and Target Recognition XXI

23 April 2012 | Baltimore, Maryland, United States

Signal Processing, Sensor Fusion, and Target Recognition XX

25 April 2011 | Orlando, Florida, United States

Signal Processing, Sensor Fusion, and Target Recognition XIX

5 April 2010 | Orlando, Florida, United States

Signal Processing, Sensor Fusion, and Target Recognition XVIII

13 April 2009 | Orlando, Florida, United States

Signal Processing, Sensor Fusion, and Target Recognition XVII

17 March 2008 | Orlando, Florida, United States

Signal Processing, Sensor Fusion, and Target Recognition XVI

9 April 2007 | Orlando, Florida, United States

Signal Processing, Sensor Fusion, and Target Recognition XV

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

Signal Processing, Sensor Fusion, and Target Recognition XIV

28 March 2005 | Orlando, Florida, United States

Signal Processing, Sensor Fusion, and Target Recognition XIII

12 April 2004 | Orlando, Florida, United States

Showing 5 of 22 Conference Committees

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