Mr. Joe Keranen Profile

Joe Keranen

at White River Technologies, Inc.

SPIE Involvement:

Author

Publications (15)

Proceedings Article | 10 May 2019 Paper

Hydro-electromagnetic properties in ground-penetrating radar field target detection studies

Gregory Schultz, Jonathan Miller, Fridon Shubitidze, Joe Keranen

Proceedings Volume 11012, 110120J (2019) https://doi.org/10.1117/12.2519196

KEYWORDS: Target detection, Radar, Soil science, Signal attenuation, Water, Dielectrics, Antennas, Electromagnetism, Environmental sensing, General packet radio service

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Proceedings Article | 30 April 2018 Paper

EMPACT 3D: an advanced EMI discrimination sensor for CONUS and OCONUS applications

Joe Keranen, Jonathan Miller, Gregory Schultz, Morgan Sander-Olhoeft, Stephen Laudato

Proceedings Volume 10628, 106280S (2018) https://doi.org/10.1117/12.2306945

KEYWORDS: 3D acquisition, Electromagnetic coupling, Polarizability, Library classification systems, Classification systems

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Proceedings Article | 3 May 2016 Paper

Dynamic EMI sensor platform for digital geophysical mapping and automated clutter rejection for CONUS and OCONUS applications

Stephen Laudato, Gregory Schultz, Joe Keranen, Jonathan Miller

Proceedings Volume 9823, 982305 (2016) https://doi.org/10.1117/12.2225027

KEYWORDS: Sensors, Metals, Error analysis, Magnetism, Receivers, Data acquisition, Magnetic sensors, Electromagnetic coupling, Electromagnetism, Polarizability

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Proceedings Article | 13 June 2014 Paper

Optimizing electromagnetic induction sensors for dynamic munitions classification surveys

Jonathan Miller, Joe Keranen, Gregory Schultz

Proceedings Volume 9072, 90720E (2014) https://doi.org/10.1117/12.2050762

KEYWORDS: Data modeling, Sensors, Magnetism, Receivers, Data acquisition, Optical resolution, Magnetic sensors, Electromagnetic coupling, Electromagnetism, Polarizability

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Proceedings Article | 9 June 2014 Paper

Acquisition and processing of advanced sensor data for ERW and UXO detection and classification

Gregory Schultz, Joe Keranen, Jonathan Miller, Fridon Shubitidze

Proceedings Volume 9072, 90720H (2014) https://doi.org/10.1117/12.2050876

KEYWORDS: Target detection, Signal to noise ratio, Sensors, Magnetism, Receivers, Data acquisition, Explosives, Neodymium, Electromagnetic coupling, Signal detection

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Proceedings Article | 9 June 2014 Paper

Advanced EMI models for survey data processing: targets detection and classification

F. Shubitidze, J. Sigman, Yinlin Wang, J. Miller, J. Keranen, I. Shamatava, B. Barrowes, K. O'Neill

Proceedings Volume 9072, 90720J (2014) https://doi.org/10.1117/12.2050897

KEYWORDS: Target detection, Transmitters, Detection and tracking algorithms, Data modeling, Sensors, Magnetism, Data processing, Electromagnetic coupling, Polarizability, Systems modeling

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One of the most challenging aspects of survey data processing is target selection. The fundamental input for the classification is dynamic data collected along survey lines. These data are different from the static data obtained in cued mode and used for target classification. Survey data are typically collected using just one transmitter loop (the Z-axis loop) and feature short data point collection times and short decay transience. The collection intervals for each data point are typically 0.1 s, and the signal repetition rates are typically 90 or 270 Hz (in other words, the transient decay times are 2.7 ms or 0.9 ms). Reliable classification requires multiple side/angle illumination; i.e., to conduct reliable classification it is necessary to combine and jointly invert multiple data points. However, picking data points that provide optimal information for classifying targets is a difficult task. The traditional method plots signal amplitudes on a 2D map and picks peaks of signal level without properly accounting for the underlying physics. In this paper, the joint diagonalization is applied to survey data sets to improve data pre-processing and target picking. The JD technique is an EMI data analysis and target classification technique and is applicable for all next-generation multi-static array EMI sensors. The method extracts multi-static response data matrix eigenvalues. The eigenvalues are main characteristics of the data. Recent studies have demonstrated that the method has great potential to quickly estimate the number of potential targets and moreover classify these targets at the data pre-processing stage, in real time and without the need for a forward model. Another advantage of JD is that it provides the ability to separate signal from noise making it possible to de-noise data without distorting the signal due to the targets. In this paper the JD technique is used to process dynamic data collected at South West Proving Ground and Aberdeen Proving Ground (APG) sites using the 2 × 2 TEMTADS and OPTEMA systems, respectively. The joint eigenvalues are extracted as functions of time for each data point and summed/stacked together before being used to create detection maps. Once targets are detected, a set of data is chosen for each anomaly and inverted using the ortho-normalized volume magnetic source technique.

Proceedings Article | 7 June 2013 Paper

Electromagnetic packable technology (EMPACT) for detection and characterization of ordnance in post-conflict areas

Gregory Schultz, Jonathan Miller, Joe Keranen

Proceedings Volume 8709, 87090K (2013) https://doi.org/10.1117/12.2016241

KEYWORDS: Target detection, Data modeling, Sensors, Magnetism, Receivers, Data acquisition, Magnetic sensors, Explosives, Electromagnetic coupling, Electromagnetism

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Proceedings Article | 7 June 2013 Paper

Operational field evaluation of the PAC-MAG man-portable magnetometer array

Joe Keranen, Zeke Topolosky, Gregory Schultz, Jonathan Miller

Proceedings Volume 8709, 87090D (2013) https://doi.org/10.1117/12.2015818

KEYWORDS: Target detection, Standoff detection, Sensors, Magnetism, Telecommunications, Magnetic sensors, Neodymium, Data communications, Magnetometers, Global Positioning System

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Proceedings Article | 11 May 2012 Paper

Inversion and classification studies of live-site production-level MetalMapper data sets

F. Shubitidze, J. Fernández, J. Miller, J. Keranen, B. Barrowes, A. Bijamov

Proceedings Volume 8357, 835704 (2012) https://doi.org/10.1117/12.919565

KEYWORDS: Mathematical modeling, Transmitters, Data modeling, Sensors, Magnetism, Data processing, Analytical research, Electromagnetic coupling, Performance modeling, Library classification systems

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Proceedings Article | 11 May 2012 Paper

Large loop EMI sensor for detection of deeply buried munitions in magnetic soils

Jonathan Miller, Stephen Billings, Joe Keranen, Gregory Schultz, Chet Bassani

Proceedings Volume 8357, 835707 (2012) https://doi.org/10.1117/12.919393

KEYWORDS: Target detection, Transmitters, Sensors, Magnetism, Receivers, Data acquisition, Magnetic sensors, Electromagnetic coupling, Polarizability, Inductance

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Proceedings Article | 23 May 2011 Paper

Magnetic sensing techniques for humanitarian ordnance detection and discrimination

Joe Keranen, Steve Billings, Gregory Schultz, Jonathan Miller

Proceedings Volume 8017, 801703 (2011) https://doi.org/10.1117/12.883974

KEYWORDS: Target detection, Sensors, Magnetism, Data acquisition, Data processing, Magnetic sensors, Magnetometers, Global Positioning System, Standards development, Land mines

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Proceedings Article | 23 May 2011 Paper

Inversion of dynamically repositioned multi-axis electromagnetic data for ordnance characterization

Joe Keranen, Fridon Shubitidze, Lance Besaw, Matthew Casari, Jonathan Miller, Gregory Schultz

Proceedings Volume 8017, 801708 (2011) https://doi.org/10.1117/12.884380

KEYWORDS: Mathematical modeling, Signal to noise ratio, Detection and tracking algorithms, Data modeling, Sensors, Magnetism, Data acquisition, Data centers, Electromagnetic coupling, Electromagnetism

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