Identification of unexploded ordnance from clutter using neural networks

Szidarovszky, Anna; Poulton, Mary M.; MacInnes, Scott C.

doi:10.1190/1.3063951

Cited by 1 publication

(6 citation statements)

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“…2007), evolutionary strategies (Zhang 2008) and artificial neural networks (Hart et al . 2000; Szidarovsky et al . 2008; Benavides et al .…”

Section: Introductionmentioning

confidence: 99%

“…Examples of supervised artificial neural networks used in UXO discrimination include probabilistic neural networks (Hart et al . 2000) and multilayer perceptrons (Szidarovsky et al . 2008).…”

Section: Introductionmentioning

confidence: 99%

“…2008). Of these, the multilayer perceptron technique was shown to distinguish between UXO and metallic clutter with high accuracy when applied to a particular synthetic data set (Szidarovsky et al . 2008).…”

Section: Introductionmentioning

confidence: 99%

“…Despite advances in numerical inverse procedures, practical applications often are limited to synthetic data or when applied to field data the false alarm rates remain unacceptably high (McKenna & Pulsipher 2009). For this reason, other non-traditional discrimination schemes have also been evaluated including algorithms based on electromagnetic induction (EMI) catalogues (Pasion et al 2007), evolutionary strategies (Zhang 2008) and artificial neural networks (Hart et al 2000;Szidarovsky et al 2008;Benavides et al 2009).…”

Section: Introductionmentioning

confidence: 99%

“…An artificial neural network analysis includes both supervised and unsupervised approaches. Examples of supervised artificial neural networks used in UXO discrimination include probabilistic neural networks (Hart et al 2000) and multilayer perceptrons (Szidarovsky et al 2008). Of these, the multilayer perceptron technique was shown to distinguish between UXO and metallic clutter with high accuracy when applied to a particular synthetic data set (Szidarovsky et al 2008).…”

Section: Introductionmentioning

confidence: 99%

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Hybrid analysis of multiaxis electromagnetic data for discrimination of munitions and explosives of concern

Friedel

Asch

Oden³

2012

Geophysical Journal International

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SUMMARY The remediation of land containing munitions and explosives of concern, otherwise known as unexploded ordnance, is an ongoing problem facing the U.S. Department of Defense and similar agencies worldwide that have used or are transferring training ranges or munitions disposal areas to civilian control. The expense associated with cleanup of land previously used for military training and war provides impetus for research towards enhanced discrimination of buried unexploded ordnance. Towards reducing that expense, a multiaxis electromagnetic induction data collection and software system, called ALLTEM, was designed and tested with support from the U.S. Department of Defense Environmental Security Technology Certification Program. ALLTEM is an on‐time time‐domain system that uses a continuous triangle‐wave excitation to measure the target‐step response rather than traditional impulse response. The system cycles through three orthogonal transmitting loops and records a total of 19 different transmitting and receiving loop combinations with a nominal spatial data sampling interval of 20 cm. Recorded data are pre‐processed and then used in a hybrid discrimination scheme involving both data‐driven and numerical classification techniques. The data‐driven classification scheme is accomplished in three steps. First, field observations are used to train a type of unsupervised artificial neural network, a self‐organizing map (SOM). Second, the SOM is used to simultaneously estimate target parameters (depth, azimuth, inclination, item type and weight) by iterative minimization of the topographic error vectors. Third, the target classification is accomplished by evaluating histograms of the estimated parameters. The numerical classification scheme is also accomplished in three steps. First, the Biot–Savart law is used to model the primary magnetic fields from the transmitter coils and the secondary magnetic fields generated by currents induced in the target materials in the ground. Second, the target response is modelled by three orthogonal dipoles from prolate, oblate and triaxial ellipsoids with one long axis and two shorter axes. Each target consists of all three dipoles. Third, unknown target parameters are determined by comparing modelled to measured target responses. By comparing the rms error among the self‐organizing map and numerical classification results, we achieved greater than 95 per cent detection and correct classification of the munitions and explosives of concern at the direct fire and indirect fire test areas at the UXO Standardized Test Site at the Aberdeen Proving Ground, Maryland in 2010.

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“…2007), evolutionary strategies (Zhang 2008) and artificial neural networks (Hart et al . 2000; Szidarovsky et al . 2008; Benavides et al .…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%