Dr. Robert G. Hutchins Profile

Dr. Robert G. Hutchins

at Naval Postgraduate School

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Publications (9)

Proceedings Article | 9 May 2008 Paper

Assurance regions in tracking

David Sworder, John Boyd, R. Hutchins

Proceedings Volume 6969, 69690J (2008) https://doi.org/10.1117/12.775820

KEYWORDS: Signal processing, Data processing, Model-based design, Motion models, Kinematics, Detection and tracking algorithms

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Proceedings Article | 19 May 2006 Paper

Studies in trajectory tracking and launch point determination for ballistic missile defense

Robert Hutchins, Philip Pace

Proceedings Volume 6236, 62360Y (2006) https://doi.org/10.1117/12.666254

KEYWORDS: Missiles, Particle filters, Detection and tracking algorithms, Error analysis, Motion models, Particles, Mathematical modeling, Defense and security, Time metrology, Electronic filtering

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Proceedings Article | 19 May 2006 Paper

Map-enhanced tracking-II

David Sworder, John Boyd, R. Hutchins

Proceedings Volume 6236, 62360A (2006) https://doi.org/10.1117/12.663536

KEYWORDS: Roads, Sensors, Motion models, Kinematics, Signal to noise ratio, Detection and tracking algorithms, Radar, Wavelets, Motion estimation, Time metrology

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Proceedings Article | 7 August 2002 Paper

Hybrid tracking algorithm using MHT and PMHT

Darin Dunham, Robert Hutchins

Proceedings Volume 4728, (2002) https://doi.org/10.1117/12.478502

KEYWORDS: Detection and tracking algorithms, Error analysis, Monte Carlo methods, Algorithm development, Data processing, Solids, Sensors, Thulium, Time metrology, Computer engineering

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Proceedings Article | 7 August 2002 Paper

Alternative architectures for IMM tracking of maneuvering aircraft

Robert Hutchins, Dean Wilson, L. Kurt Allred, Russell Duren

Proceedings Volume 4728, (2002) https://doi.org/10.1117/12.478513

KEYWORDS: Detection and tracking algorithms, Motion models, Filtering (signal processing), Motion estimation, Mathematical modeling, 3D modeling, Electronic filtering, Solids, Coastal modeling, Signal processing

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Proceedings Article | 4 October 1999 Paper

Trajectory tracking and backfitting techniques against theater ballistic missiles

Robert Hutchins, Patrick Britt

Proceedings Volume 3809, (1999) https://doi.org/10.1117/12.364053

KEYWORDS: Missiles, Motion models, 3D modeling, 3D metrology, Error analysis, Electronic filtering, Systems modeling, Mathematical modeling, Signal processing, Weapons of mass destruction

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Proceedings Article | 3 September 1998 Paper

IMM tracking of a theater ballistic missile during boost phase

Robert Hutchins, Anthony San Jose

Proceedings Volume 3373, (1998) https://doi.org/10.1117/12.324646

KEYWORDS: Missiles, Detection and tracking algorithms, Motion models, Computer simulations, Data modeling, Switching, Sensors, Solids, 3D metrology, Weapons of mass destruction

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Proceedings Article | 23 June 1997 Paper

Tracking multiple targets in cluttered environments with a probabilistic multihypothesis tracker

Darin Dunham, Robert Hutchins

Proceedings Volume 3086, (1997) https://doi.org/10.1117/12.277188

KEYWORDS: Detection and tracking algorithms, Algorithm development, Solids, Data processing, Environmental sensing, Electronic filtering, Error analysis, Computer simulations, Logic, Analytical research

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Tracking multiple targets in a cluttered environment is extremely difficult. Traditional approaches use simple techniques to determine what are the true measurements by a combination of gating and some form of a nearest neighbor association. As clutter densities increase, these traditional algorithms fail to perform well. To counter this problem, the multi-hypothesis tracking (MHT) algorithm was developed. This approach enumerates almost every conceivable possible combination of measurements to determine the most likely. This process quickly becomes very complex and requires vast amounts of memory in order to store all of the possible tracks. To avoid this complexity, more sophisticated single hypothesis data association techniques have been developed, such as the probabilistic data association filter (PDAF). These algorithms have enjoyed some success but do not take advantage of any future data to help clarify ambiguous situations. On the other hand, the probabilistic multi-hypothesis tracking (PMHT) algorithm, proposed by Streit and Luginbuhl in 1995, attempts to use the best aspects of the MHT and the PDAF. In the PMHT algorithm, data is processed in batches, thereby using information from before and after each measurement to determine the likelihood of each measurement-to-track association. Furthermore, like the PDAF, it does not attempt to make hard assignments or enumerate all possible combinations. but instead associates each measurement with each track based upon its probability of association. Actual performance and initialization of the PMHT algorithm in the presence of significant clutter has not been adequately researched. This study focuses on the performance of the PMHT algorithm in dense clutter and the initialization thereof. In addition, the effectiveness of measurement attribute data is analyzed, especially as it relates to algorithm initialization. Further, it compares the performance of this algorithm to the nearest neighbor, MHT, and PDAF.

Proceedings Article | 6 July 1994 Paper

Image-enhanced passive tracking of maneuvering targets

Robert Hutchins

Proceedings Volume 2235, (1994) https://doi.org/10.1117/12.179079

KEYWORDS: Image processing, Motion models, Sensors, Imaging systems, Optical sensors, Nonlinear filtering, Image filtering, Motion measurement, Monte Carlo methods, Systems modeling

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Showing 5 of 9 publications

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