Dr. Benjamin Slocumb Profile

Dr. Benjamin Slocumb

Vice President of Programs and Business Dev at Numerica Corp

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Publications (17)

Proceedings Article | 16 April 2010 Paper

A tracker adjunct processing system for reconsideration of firm tracker decisions

David Trawick, Benjamin Slocumb, Randy Paffenroth

Proceedings Volume 7698, 76980N (2010) https://doi.org/10.1117/12.852461

KEYWORDS: Kinematics, Sensors, Data modeling, Telecommunications, Motion models, Computing systems, Filtering (signal processing), Electronic filtering, Detection and tracking algorithms, Data processing

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Most modern maximum likelihood multiple target tracking systems (e.g., Multiple Hypothesis Tracking (MHT) and Numerica's Multiple Frame Assignment (MFA)) need to determine how to separate their input measurements into subsets corresponding to the observations of individual targets. These observation sets form the tracks of the system, and the process of determining these sets is known as data association. Real-time constraints frequently force the use of only the maximum likelihood choice for data association (over some time window), although alternative data association choices may have been considered in the process of choosing the most likely. This paper presents a Tracker Adjunct Processing (TAP) system that captures and manages the uncertainty encountered in making data association decisions. The TAP combines input observation data and the data association alternatives considered by the tracker into a dynamic Bayesian network (DBN). The network efficiently represents the combined alternative tracking hypotheses. Bayesian network evidence propagation methods are used to update the network in light of new evidence, which may consist of new observations, new alternative data associations, newly received late observations, hypothetical connections, or other flexible queries. The maximum likelihood tracking hypothesis can then be redetermined, which may result in changes to the best tracking hypothesis. The recommended changes can then be communicated back to the associated tracking system, which can then update its tracks. In this manner, the TAP's interpretation makes the firm, fixed (formerly maximum likelihood) decisions of the tracker "softer," i.e., less absolute. The TAP can also assess (and reassess) track purity regions by ambiguity level. We illustrate the working of the TAP with several examples, one in particular showing the incorporation of critical, late or infrequent data. These data are critical in the sense that they are very valuable in resolving ambiguities in tracking and combat identification; thus, the motivation to use these data is high even though there are complexities in applying it. Some data may be late because of significant network delays, while other data may be infrequently reported because they come from "specialized" sensors that provide updates only every once in a while.

Proceedings Article | 16 April 2010 Paper

Association ambiguity management in mixed data dimension tracking problems

James Thornbrue, J. Nate Knight, Benjamin Slocumb

Proceedings Volume 7698, 76980M (2010) https://doi.org/10.1117/12.851177

KEYWORDS: Radar, Sensors, Target detection, Filtering (signal processing), Error analysis, Monte Carlo methods, Detection and tracking algorithms, Time metrology, Radar sensor technology, Statistical analysis

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Proceedings Article | 19 May 2006 Paper

Complexity reduction in MHT/MFA tracking: part II, hierarchical implementation and simulation results

Benjamin Slocumb, Aubrey Poore

Proceedings Volume 6236, 62360U (2006) https://doi.org/10.1117/12.673520

KEYWORDS: Radar, Filtering (signal processing), Detection and tracking algorithms, Doppler effect, Surveillance, Data processing, Monte Carlo methods, Sensors, Algorithm development, Computer simulations

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Proceedings Article | 19 May 2006 Paper

Surveillance radar range-bearing centroid processing, part II: merged measurements

Benjamin Slocumb, Daniel Macumber

Proceedings Volume 6236, 623604 (2006) https://doi.org/10.1117/12.673521

KEYWORDS: Expectation maximization algorithms, Radar, Detection and tracking algorithms, Surveillance, Target detection, Antennas, Algorithm development, Signal processing, Sensors, Computer simulations

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Proceedings Article | 16 September 2005 Paper

Frame-building algorithm for electronically scanned array radar

Benjamin Slocumb, Michael Klusman

Proceedings Volume 5913, 59130H (2005) https://doi.org/10.1117/12.615279

KEYWORDS: Detection and tracking algorithms, Radar, Sensors, Databases, Signal to noise ratio, Antennas, Computer simulations, Surveillance, Data processing, Logic

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Proceedings Article | 15 September 2005 Paper

A multiple model SNR/RCS likelihood ratio score for radar-based feature-aided tracking

Benjamin Slocumb, Michael Klusman

Proceedings Volume 5913, 59131N (2005) https://doi.org/10.1117/12.615288

KEYWORDS: Digital filtering, Signal to noise ratio, Radar, Electronic filtering, Kinematics, Data modeling, Data processing, Time metrology, Statistical modeling, Smoothing

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Proceedings Article | 25 August 2004 Paper

Multiple hypothesis clustering and multiple frame assignment tracking

Sabino Gadaleta, Aubrey Poore, Sean Roberts, Benjamin Slocumb

Proceedings Volume 5428, (2004) https://doi.org/10.1117/12.542213

KEYWORDS: Expectation maximization algorithms, Sensors, Missiles, Detection and tracking algorithms, Computer simulations, Nickel, Logic, Computing systems, Monte Carlo methods, Defense and security

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Proceedings Article | 5 January 2004 Paper

Batch maximum likelihood (ML) and maximum a posteriori (MAP) estimation with process noise for tracking applications

Aubrey Poore, Benjamin Slocumb, Brian Suchomel, Fritz Obermeyer, Shawn Herman, Sabino Gadaleta

Proceedings Volume 5204, (2004) https://doi.org/10.1117/12.506442

KEYWORDS: Filtering (signal processing), Electronic filtering, Velocity measurements, Nonlinear filtering, Signal processing, Performance modeling, Matrices, Statistical modeling, Error analysis, Linear algebra

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Proceedings Article | 5 January 2004 Paper

Maximum likelihood narrowband radar data segmentation and centroid processing

Benjamin Slocumb, William Blair

Proceedings Volume 5204, (2004) https://doi.org/10.1117/12.521013

KEYWORDS: Radar, Detection and tracking algorithms, Target detection, Algorithm development, Signal processing, Radar signal processing, Signal to noise ratio, Computer simulations, Data processing, Switches

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Proceedings Article | 5 January 2004 Paper

Pixel-cluster decomposition tracking for multiple IR-sensor surveillance

Sabino Gadaleta, Aubrey Poore, Benjamin Slocumb

Proceedings Volume 5204, (2004) https://doi.org/10.1117/12.502731

KEYWORDS: Sensors, Particles, Expectation maximization algorithms, Detection and tracking algorithms, Infrared sensors, Point spread functions, Image resolution, Monte Carlo methods, Signal to noise ratio, Optical resolution

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Proceedings Article | 7 August 2002 Paper

EM-based measurement fusion for HRR radar centroid processing

Benjamin Slocumb, W. Dale Blair

Proceedings Volume 4728, (2002) https://doi.org/10.1117/12.478540

KEYWORDS: Expectation maximization algorithms, Radar, Detection and tracking algorithms, Algorithm development, Target detection, Missiles, Data processing, Signal to noise ratio, Scattering, Data fusion

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Proceedings Article | 7 August 2002 Paper

Multiple frame cluster tracking

Sabino Gadaleta, Mike Klusman, Aubrey Poore, Benjamin Slocumb

Proceedings Volume 4728, (2002) https://doi.org/10.1117/12.478511

KEYWORDS: Expectation maximization algorithms, Data modeling, Detection and tracking algorithms, Sensors, Missiles, Algorithm development, Target detection, Radar, Statistical modeling, Fused deposition modeling

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Proceedings Article | 26 November 2001 Paper

Surveillance radar range-bearing centroid processing

Benjamin Slocumb

Proceedings Volume 4473, (2001) https://doi.org/10.1117/12.492759

KEYWORDS: Radar, Target detection, Detection and tracking algorithms, Doppler effect, Surveillance, Antennas, Signal to noise ratio, Beam analyzers, Logic, Beam shaping

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Proceedings Article | 13 July 2000 Paper

Implementation and analysis of the decomposition-fusion ECCM technique

Benjamin Slocumb, Philip West, X. Rong Li

Proceedings Volume 4048, (2000) https://doi.org/10.1117/12.392001

KEYWORDS: Radar, Electronic filtering, Signal to noise ratio, Target detection, Detection and tracking algorithms, Performance modeling, Monte Carlo methods, Algorithm development, Systems modeling, Data fusion

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Proceedings Article | 4 October 1999 Paper

Tracking in the presence of range deception ECM and clutter by decomposition and fusion

X. Rong Li, Benjamin Slocumb, Philip West

Proceedings Volume 3809, (1999) https://doi.org/10.1117/12.364050

KEYWORDS: Radar, Detection and tracking algorithms, Target detection, Electronic filtering, Skin, Filtering (signal processing), Signal processing, Holmium, Aluminum, Binary data

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Proceedings Article | 28 July 1997 Paper

Pulse train PDA analysis and deinterleaving filter

Benjamin Slocumb, Edward Kamen

Proceedings Volume 3068, (1997) https://doi.org/10.1117/12.280808

KEYWORDS: Personal digital assistants, Filtering (signal processing), Receivers, Radar, Data modeling, Pulse filters, Statistical analysis, Monte Carlo methods, Signal to noise ratio, Electronic filtering

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Proceedings Article | 1 September 1990 Paper

Maximum likelihood estimation applied to quantum-limited optical position-sensing

B. Slocumb

Proceedings Volume 1304, (1990) https://doi.org/10.1117/12.2322208

KEYWORDS: Expectation maximization algorithms, Signal to noise ratio, Sensors, Error analysis, Detector arrays, Receivers, Optical tracking, Photodetectors, Process modeling, Data modeling

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Showing 5 of 17 publications

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