Optimizing electromagnetic induction sensors for dynamic munitions classification surveys

Jonathan S. Miller; Joe Keranen; Gregory Schultz

doi:10.1117/12.2050762

13 June 2014 Optimizing electromagnetic induction sensors for dynamic munitions classification surveys

Jonathan S. Miller, Joe Keranen, Gregory Schultz

Author Affiliations +

Proceedings Volume 9072, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XIX; 90720E (2014) https://doi.org/10.1117/12.2050762
Event: SPIE Defense + Security, 2014, Baltimore, MD, United States

Abstract

Standard protocol for detection and classification of Unexploded Ordnance (UXO) comprises a two-step process that includes an initial digital geophysical mapping (DGM) survey to detect magnetic field anomalies followed by a cued survey at each anomaly location that enables classification of these anomalies. The initial DGM survey is typically performed using a low resolution single axis electromagnetic induction (EMI) sensor while the follow-up cued survey requires revisiting each anomaly location with a multi-axis high resolution EMI sensor. The DGM survey comprises data collection in tightly spaced transects over the entire survey area. Once data collection in this area is complete, a threshold analysis is applied to the resulting magnetic field anomaly map to identify anomalies corresponding to potential targets of interest (TOI). The cued sensor is deployed in static mode where this higher resolution sensor is placed over the location of each anomaly to record a number of soundings that may be stacked and averaged to produce low noise data. These data are of sufficient quality to subsequently classify the object as either TOI or clutter. While this approach has demonstrated success in producing effective classification of UXO, conducting successive surveys is time consuming. Additionally, the low resolution of the initial DGM survey often produces errors in the target picking process that results in poor placement of the cued sensor and often requires several revisits to the anomaly location to ensure adequate characterization of the target space. We present data and test results from an advanced multi-axis EMI sensor optimized to provide both detection and classification from a single survey. We demonstrate how the large volume of data from this sensor may be used to produce effective detection and classification decisions while only requiring one survey of the munitions response area.

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Jonathan S. Miller, Joe Keranen, and Gregory Schultz "Optimizing electromagnetic induction sensors for dynamic munitions classification surveys", Proc. SPIE 9072, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XIX, 90720E (13 June 2014); https://doi.org/10.1117/12.2050762

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KEYWORDS

Sensors

Polarizability

Electromagnetic coupling

Magnetism

Magnetic sensors

Data modeling

Data acquisition

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