Dr. Clay J. Stanek Profile

Dr. Clay J. Stanek

Chief Mathematician & Technical Fellow

SPIE Involvement:

Author | Instructor

Proceedings Article | 5 October 2007 Paper

Failure prediction for satellite monitoring systems using Bayesian networks

Steven Bottone, Daniel Lee, Clay Stanek, Michael O'Sullivan, Mark Spivack

Proceedings Volume 6736, 67360G (2007) https://doi.org/10.1117/12.738114

KEYWORDS: Failure analysis, Satellites, Amplifiers, Data modeling, Complex systems, Data analysis, Statistical analysis, Systems modeling, Control systems, Data conversion

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Proceedings Article | 20 September 2007 Paper

Failure prediction in monitoring and control systems using Bayesian networks

Steven Bottone, Clay Stanek

Proceedings Volume 6695, 66950U (2007) https://doi.org/10.1117/12.734984

KEYWORDS: Failure analysis, Satellites, Amplifiers, Data modeling, Control systems, Complex systems, Data analysis, Data conversion, Antennas, Diagnostics

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Proceedings Article | 7 May 2007 Paper

Probabilistic graphical models and their application in data fusion

Steven Bottone, Clay Stanek

Proceedings Volume 6566, 65660L (2007) https://doi.org/10.1117/12.722568

KEYWORDS: Probability theory, Data modeling, Electronic signals intelligence, Data fusion, Sensors, Decision support systems, Systems modeling, Algorithms, Data analysis, Intelligent sensors

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Proceedings Article | 16 September 2003 Paper

Performance assessment of frequency plane filters applied to track association and sensor registration

Clay Stanek, Bahram Javidi, P. Yanni

Proceedings Volume 5094, (2003) https://doi.org/10.1117/12.499599

KEYWORDS: Sensors, Image filtering, Detection and tracking algorithms, Radar, Data modeling, Electronic filtering, Kinematics, Pattern recognition, Linear filtering, Image processing

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Proceedings Article | 27 November 2002 Paper

Filter construction for topological track association and sensor registration

Clay Stanek, Bahram Javidi, Aleksandr Skorokhod, Philip Yanni

Proceedings Volume 4789, (2002) https://doi.org/10.1117/12.467794

KEYWORDS: Sensors, Image filtering, Nonlinear filtering, Composites, Optical correlators, Image sensors, Radar, Image registration, Detection and tracking algorithms, Optical tracking

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Proceedings Article | 25 July 2002 Paper

Image-based topology for sensor gridlocking and association

Clay Stanek, Bahram Javidi, Philip Yanni

Proceedings Volume 4726, (2002) https://doi.org/10.1117/12.477045

KEYWORDS: Sensors, Composites, Detection and tracking algorithms, Error analysis, Optical correlators, Image filtering, Radar, Navigation systems, Sensor fusion, Filtering (signal processing)

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Correlation engines have been evolving since the implementation of radar. In modern sensor fusion architectures, correlation and gridlock filtering are required to produce common, continuous, and unambiguous tracks of all objects in the surveillance area. The objective is to provide a unified picture of the theatre or area of interest to battlefield decision makers, ultimately enabling them to make better inferences for future action and eliminate fratricide by reducing ambiguities. Here, correlation refers to association, which in this context is track-to-track association. A related process, gridlock filtering or gridlocking, refers to the reduction in navigation errors and sensor misalignment errors so that one sensor's track data can be accurately transformed into another sensor's coordinate system. As platforms gain multiple sensors, the correlation and gridlocking of tracks become significantly more difficult. Much of the existing correlation technology revolves around various interpretations of the generalized Bayesian decision rule: choose the action that minimizes conditional risk. One implementation of this principle equates the risk minimization statement to the comparison of ratios of a priori probability distributions to thresholds. The binary decision problem phrased in terms of likelihood ratios is also known as the famed Neyman-Pearson hypothesis test. Using another restatement of the principle for a symmetric loss function, risk minimization leads to a decision that maximizes the a posteriori probability distribution. Even for deterministic decision rules, situations can arise in correlation where there are ambiguities. For these situations, a common algorithm used is a sparse assignment technique such as the Munkres or JVC algorithm. Furthermore, associated tracks may be combined with the hope of reducing the positional uncertainty of a target or object identified by an existing track from the information of several fused/correlated tracks. Gridlocking is typically accomplished with some type of least-squares algorithm, such as the Kalman filtering technique, which attempts to locate the best bias error vector estimate from a set of correlated/fused track pairs. Here, we will introduce a new approach to this longstanding problem by adapting many of the familiar concepts from pattern recognition, ones certainly familiar to target recognition applications. Furthermore, we will show how this technique can lend itself to specialized processing, such as that available through an optical or hybrid correlator.

Proceedings Article | 19 July 1999 Paper

Considerations and algorithm development for scene-based nonuniformity correction (NUC) in dynamic infrared projectors

Clay Stanek, Larry Ewing, Douglas Moore

Proceedings Volume 3697, (1999) https://doi.org/10.1117/12.352917

KEYWORDS: Nonuniformity corrections, Calibration, Sensors, Staring arrays, Projection systems, Infrared radiation, Infrared imaging, Radiometry, Cameras, Image sensors

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Proceedings Article | 13 July 1998 Paper

Analysis and implications of resistive emitter array non-uniformity correction (NUC) between sensors with different spectral bands

Clay Stanek, Douglas Moore, Ronald Driggers

Proceedings Volume 3368, (1998) https://doi.org/10.1117/12.316362

KEYWORDS: Sensors, Calibration, Nonuniformity corrections, Black bodies, Temperature metrology, Radiometry, Colorimetry, Staring arrays, Infrared radiation, Infrared imaging

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Proceedings Article | 15 July 1997 Paper

Performance limitations and practical considerations associated with resistive emitter array nonuniformity correction (NUC)

Douglas Moore, Clay Stanek, Curt Peterson

Proceedings Volume 3084, (1997) https://doi.org/10.1117/12.280942

KEYWORDS: Nonuniformity corrections, Sensors, Staring arrays, Digital signal processing, Calibration, Error analysis, Infrared radiation, Cameras, Infrared imaging, Image sensors

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Showing 5 of 9 publications

Conference Committee Involvement (7)

Optics and Photonics for Information Processing III

4 August 2009 | San Diego, California, United States

Optics and Photonics for Information Processing II

13 August 2008 | San Diego, California, United States

Optics and Photonics for Information Processing

28 August 2007 | San Diego, California, United States

Optical Information Systems IV

16 August 2006 | San Diego, California, United States

Optical Information Systems III

3 August 2005 | San Diego, California, United States

Showing 5 of 7 Conference Committees

Course Instructor

SC189: Image Recognition Using Statistical Filtering Techniques, Wavelets and Neural Networks

Image processing and recognition is one of the important applications of information systems. This course is aimed at people interested to learn practical applications of image processing techniques applied to real-time applications including biomedical image processing and image recognition problems. This course will review fundamentals of digital image processing, imaging systems, image recognition, statistical filtering for image processing, fundamentals of wavelet transforms for image processing, fundamentals of neural networks for image processing, and recent advances in image recognition techniques. The course will present examples on applications of these techniques in real-time pattern recognition, target tracking, classification, and real-time biometrics recognition.

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