Mr. Darin T. Dunham Profile

Darin T. Dunham

Owner/Senior Research Engineer

SPIE Involvement:

Author

Proceedings Article | 13 June 2014 Paper

Advancement of an algorithm

Darin Dunham, Terry Ogle, Peter Willett, Balakumar Balasingam

Proceedings Volume 9092, 90920K (2014) https://doi.org/10.1117/12.2051355

KEYWORDS: Detection and tracking algorithms, Expectation maximization algorithms, Algorithm development, Staring arrays, Target detection, Data modeling, Algorithms, Sensors, Infrared sensors, Monte Carlo methods

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Proceedings Article | 30 September 2013 Paper

Multiple target tracking from images using the maximum likelihood HPMHT

Peter Willett, Balakumar Balasingam, Darin Dunham, Terry Ogle

Proceedings Volume 8857, 88570L (2013) https://doi.org/10.1117/12.2027297

KEYWORDS: Staring arrays, Photons, Signal to noise ratio, Target detection, Data modeling, Point spread functions, Infrared imaging, Sensors, Detection and tracking algorithms, Infrared radiation

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Proceedings Article | 15 May 2012 Paper

PMHT for fused tracking

Darin Dunham, Terry Ogle, Peter Willett

Proceedings Volume 8393, 83930G (2012) https://doi.org/10.1117/12.921198

KEYWORDS: Sensors, Missiles, Expectation maximization algorithms, 3D acquisition, 3D metrology, Passive sensors, Detection and tracking algorithms, Monte Carlo methods, Annealing, Environmental sensing

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Proceedings Article | 17 September 2011 Paper

Benchmarks for target tracking

Darin Dunham, Philip West

Proceedings Volume 8137, 81370V (2011) https://doi.org/10.1117/12.894411

KEYWORDS: Detection and tracking algorithms, Algorithm development, Sensors, Radar, Missiles, Monte Carlo methods, Defense and security, Performance modeling, Systems modeling, Computer simulations

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Proceedings Article | 16 April 2010 Paper

Using PMHT for separation point estimation

Darin Dunham, Scott August

Proceedings Volume 7698, 76980P (2010) https://doi.org/10.1117/12.851468

KEYWORDS: Detection and tracking algorithms, Sensors, Monte Carlo methods, Radar, Error analysis, Expectation maximization algorithms, Time metrology, Performance modeling, Received signal strength, Optical resolution

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Proceedings Article | 21 September 2007 Paper

Simulation assessment of RCS-aided multiple target tracking

Darin Dunham, Lisa Ehrman, W. Dale Blair, Susan Frost

Proceedings Volume 6699, 66991D (2007) https://doi.org/10.1117/12.740892

KEYWORDS: Signal to noise ratio, Target detection, Sensors, Switches, Kinematics, Radar, Mahalanobis distance, Signal processing, Time metrology, Detection and tracking algorithms

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Proceedings Article | 21 September 2007 Paper

Consistent covariance estimation for PMHT

Wayne Blanding, Peter Willett, Roy Streit, Darin Dunham

Proceedings Volume 6699, 66990I (2007) https://doi.org/10.1117/12.738495

KEYWORDS: Expectation maximization algorithms, Detection and tracking algorithms, Error analysis, Monte Carlo methods, Statistical analysis, Matrices, Computer simulations, Weapons, Data processing, Personal digital assistants

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Proceedings Article | 19 May 2006 Paper

Distributed multiple sensor tracking with the PMHT

Darin Dunham

Proceedings Volume 6236, 62360X (2006) https://doi.org/10.1117/12.669056

KEYWORDS: Sensors, Composites, Detection and tracking algorithms, Missiles, Monte Carlo methods, X band, Radar, Switches, Error analysis, Coastal modeling

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Proceedings Article | 15 September 2005 Paper

Solving the distributed multiple platform tracking problem with the PMHT algorithm

Darin Dunham

Proceedings Volume 5913, 59131C (2005) https://doi.org/10.1117/12.615721

KEYWORDS: Detection and tracking algorithms, Composites, Sensors, Missiles, Radar, Electronic filtering, Error analysis, Analytical research, Thulium, Filtering (signal processing)

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Proceedings Article | 25 August 2004 Paper

Multiple hypothesis tracking for a distributed multiple platform system

Darin Dunham, Samuel Blackman, Robert Dempster

Proceedings Volume 5428, (2004) https://doi.org/10.1117/12.541613

KEYWORDS: Logic, Missiles, Distributed computing, Composites, Radar, Algorithm development, Data communications, Sensors, Detection and tracking algorithms, Electroluminescence

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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 | 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.

Showing 5 of 12 publications

Conference Committee Involvement (1)

IEEE Aerospace Conference

3 March 2012 |

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