Prof. Stanton H. Musick Profile

Prof. Stanton H. Musick

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Publications (13)

Proceedings Article | 25 May 2005 Paper

Nonlinear filtering for tracking large objects in radar imagery

John Greenewald, Stanton Musick

Proceedings Volume 5809, (2005) https://doi.org/10.1117/12.603862

KEYWORDS: Radar, Particles, Signal to noise ratio, Kinematics, Target detection, Nonlinear filtering, Doppler effect, Image segmentation, Sensors, Scattering

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Detecting and tracking a moving ground target in radar imagery is a challenge intensified by clutter, sensor anomalies, and the substantial signature variations that occur when a target's aspect angle changes rapidly. In its GMTI mode, a radar produces range-Doppler images that contain both kinematic reports and shape features. An HRR signature, when formed as the Fourier transform of the range-Doppler image across its Doppler dimension, becomes a derived measurement and an alternative source of identity information. Although HRR signatures can vary enormously with even small changes in target aspect, such signatures were vital for associating kinematic reports to tracks in this work. This development started with video phase history (VPH) data recorded from a live experiment involving a GMTI radar viewing a single moving target. Since the target could appear anywhere in the range-Doppler image derived from the VPH data, the goal was to localize it in a small range-Doppler "chip" that could be extracted and used in subsequent research. Although the clutter in any given VPH frame generally caused false chips to be formed in the full range-Doppler image, at most one chip contained the target. The most effective approach for creating any chip is to ensure that the object is present in the return from each pulse that contributes to that chip, and to correct any phase distortions arising from range gate changes. Processing constraints dictated that the algorithm for target chip extraction be coded in MATLAB with a time budget of a few seconds per frame. Furthermore, templates and shape models to describe the target were prohibited. This paper describes the nonlinear filtering approach used to reason over multiple frames of VPH data. This nonlinear approach automatically detects and segments potential targets in the range-Doppler imagery, and then extracts kinematic and shape features that are tracked over multiple data frames to ensure that the real target is in the declared chip. The algorithm described was used successfully to process over 84,000 frames of real data without human assistance.

Proceedings Article | 19 May 2005 Paper

Radar signature generation for feature-aided tracking research

Teri Piatt, John Sherwood, Stanton Musick

Proceedings Volume 5808, (2005) https://doi.org/10.1117/12.606188

KEYWORDS: Synthetic aperture radar, Radar, Sensors, Data modeling, Seaborgium, Databases, Monte Carlo methods, Electro optical modeling, Reflectors, Solid modeling

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Proceedings Article | 2 September 2004 Paper

A simulation system for feature-aided tracking research

Stanton Musick, John Sherwood, Teri Piatt, Neal Carlson

Proceedings Volume 5427, (2004) https://doi.org/10.1117/12.548005

KEYWORDS: Sensors, Solid modeling, Synthetic aperture radar, Radar, Monte Carlo methods, Reflectors, Computer aided design, Seaborgium, Data modeling, Detection and tracking algorithms

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Proceedings Article | 25 August 2003 Paper

Robust SAR ATR via set-valued classifiers: new results

John Hoffman, Ronald Mahler, Ravi Ravichandran, Raman Mehra, Stanton Musick

Proceedings Volume 5096, (2003) https://doi.org/10.1117/12.488540

KEYWORDS: Synthetic aperture radar, Detection and tracking algorithms, Fuzzy logic, Automatic target recognition, Target recognition, Error analysis, Statistical analysis, Statistical modeling, Data modeling, Analytical research

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Proceedings Article | 31 July 2002 Paper

Robust SAR ATR by hedging against uncertainty

John Hoffman, Ronald Mahler, Ravi Ravichandran, Melvyn Huff, Stanton Musick

Proceedings Volume 4729, (2002) https://doi.org/10.1117/12.477605

KEYWORDS: Synthetic aperture radar, Matrices, Fuzzy logic, Detection and tracking algorithms, Sensors, Automatic target recognition, Analytical research, Signal processing, Algorithm development, Error analysis

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Proceedings Article | 31 July 2002 Paper

Multiple-frame multiple-hypothesis method for tracking at low SNR

John Greenewald, Stanton Musick

Proceedings Volume 4729, (2002) https://doi.org/10.1117/12.477620

KEYWORDS: Signal to noise ratio, Sensors, Monte Carlo methods, Nonlinear filtering, Detection and tracking algorithms, Image sensors, Particle filters, Astatine, Kinematics, Analytical research

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Proceedings Article | 16 August 2001 Paper

Unified generalized Bayesian accrual of evidence for robust ATR: new results

John Hoffman, Ronald Mahler, Ravi Prasanth, Melvyn Huff, Ravi Ravichandran, Raman Mehra, Stanton Musick

Proceedings Volume 4380, (2001) https://doi.org/10.1117/12.436940

KEYWORDS: Automatic target recognition, Synthetic aperture radar, Detection and tracking algorithms, Error analysis, Estimation theory, Fuzzy logic, Algorithm development, Electronic filtering, Filtering (signal processing), Firearms

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Proceedings Article | 4 August 2000 Paper

Track and bias estimation without data association

Stanton Musick, Keith Kastella

Proceedings Volume 4052, (2000) https://doi.org/10.1117/12.395091

KEYWORDS: Sensors, Nonlinear filtering, Error analysis, Image sensors, Signal to noise ratio, Filtering (signal processing), Motion models, Monte Carlo methods, Stochastic processes, Data analysis

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Proceedings Article | 4 August 2000 Paper

Joint tracking, pose estimation, and identification using HRRR data

Ronald Mahler, Constantino Rago, Tim Zajic, Stanton Musick, Raman Mehra

Proceedings Volume 4052, (2000) https://doi.org/10.1117/12.395070

KEYWORDS: Detection and tracking algorithms, Nonlinear filtering, Electronic filtering, Target recognition, Radar, Statistical analysis, Automatic target recognition, Filtering (signal processing), Optical filters, Target detection

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Proceedings Article | 4 August 2000 Paper

Unified evidence accrual for SAR: recent results

Melvyn Huff, Ssu-Hsin Yu, Ronald Mahler, B. Ravichandran, Raman Mehra, Stanton Musick

Proceedings Volume 4052, (2000) https://doi.org/10.1117/12.395066

KEYWORDS: Synthetic aperture radar, Automatic target recognition, Data modeling, Fuzzy logic, Nonlinear filtering, Performance modeling, Target recognition, 3D modeling, Statistical analysis, Detection and tracking algorithms

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Proceedings Article | 27 July 1999 Paper

Application of unified evidence accrual methods to robust SAR ATR

Ronald Mahler, Ssu-Hsin Yu, Raman Mehra, Ravi Ravichandran, Stanton Musick

Proceedings Volume 3720, (1999) https://doi.org/10.1117/12.357194

KEYWORDS: Automatic target recognition, Synthetic aperture radar, Systems modeling, Model-based design, Fuzzy logic, Performance modeling, Detection and tracking algorithms, Nonlinear filtering, Algorithm development, Statistical analysis

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Proceedings Article | 17 July 1998 Paper

Practical implementation of joint multitarget probabilities

Stanton Musick, Keith Kastella, Ronald Mahler

Proceedings Volume 3374, (1998) https://doi.org/10.1117/12.327111

KEYWORDS: Sensors, Target detection, Monte Carlo methods, Thulium, Signal to noise ratio, Surveillance, Target recognition, Mathematics, Motion models, Binary data

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Proceedings Article | 31 May 1996 Paper

Search for optimal sensor management

Keith Kastella, Stanton Musick

Proceedings Volume 2759, (1996) https://doi.org/10.1117/12.241194

KEYWORDS: Sensors, Target detection, Surveillance, Signal to noise ratio, Radar, Monte Carlo methods, Silicon, Detection and tracking algorithms, Kinematics, Signal detection

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Showing 5 of 13 publications

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