Adapting physically complete models to vehicle-based EMI array sensor data: data inversion and discrimination studies

This paper reports vehicle based electromagnetic induction (EMI) array sensor data inversion and discrimination results. Recent field studies show that EMI arrays, such as the Minelab Single Transmitter Multiple Receiver (STMR), and the Geophex GEM-5 EMI array, provide a fast and safe way to detect subsurface metallic targets such as landmines, unexploded ordnance (UXO) and buried explosives. The array sensors are flexible and easily adaptable for a variety of ground vehicles and mobile platforms, which makes them very attractive for safe and cost effective detection operations in many applications, including but not limited to explosive ordnance disposal and humanitarian UXO and demining missions. Most state-of-the-art EMI arrays measure the vertical or full vector field, or gradient tensor fields and utilize them for real-time threat detection based on threshold analysis. Real field practice shows that the threshold-level detection has high false alarms. One way to reduce these false alarms is to use EMI numerical techniques that are capable of inverting EMI array data in real time. In this work a physically complete model, known as the normalized volume/surface magnetic sources (NV/SMS) model is adapted to the vehicle-based EMI array, such as STMR and GEM-5, data. The NV/SMS model can be considered as a generalized volume or surface dipole model, which in a special limited case coincides with an infinitesimal dipole model approach. According to the NV/SMS model, an object's response to a sensor's primary field is modeled mathematically by a set of equivalent magnetic dipoles, distributed inside the object (i.e. NVMS) or over a surface surrounding the object (i.e. NSMS). The scattered magnetic field of the NSMS is identical to that produced by a set of interacting magnetic dipoles. The amplitudes of the magnetic dipoles are normalized to the primary magnetic field, relating induced magnetic dipole polarizability and the primary magnetic field. The magnitudes of the NSMS are determined directly by minimizing the difference between measured and modeled data for any known object and any type of EMI sensor data. The EMI array data are inverted via the combined NV/SMS and differential evolution inversion method that uses a search scheme to estimate the location of the target. First, the applicability of the NV/SMS and DE algorithms to STMR and GEM-5 data sets is demonstrated by comparing the modeled data against the actual data, and finally the discrimination studies are conducted using as discrimination parameters the total NV/SMS and the principal axis of the induced magnetic polarizability tensor for each target.

[1]  F. Shubitidze,et al.  Simple magnetic charge model for representation of emi responses from a buried UXO , 2004, Proceedings of the 9th International Seminar/Workshop on Direct and Inverse Problems of Electromagnetic and Acoustic Wave Theory, 2004. DIPED 2004..

[2]  Irma Shamatava,et al.  Fast and accurate calculation of physically complete EMI response by a heterogeneous metallic object , 2005, IEEE Transactions on Geoscience and Remote Sensing.

[3]  Leslie M. Collins,et al.  Sensing of unexploded ordnance with magnetometer and induction data: theory and signal processing , 2003, IEEE Trans. Geosci. Remote. Sens..

[4]  Irma Shamatava,et al.  A Simple Magnetic Charge Model for Classification of Multiple Buried Metallic Objects In Cases With Overlapping Signals , 2005 .

[5]  Rainer Storn,et al.  System design by constraint adaptation and differential evolution , 1999, IEEE Trans. Evol. Comput..

[6]  Leonard R. Pasion,et al.  Computing Transient Electromagnetic Responses of a Metallic Object Using a Spheroidal Excitation Approach , 2008, IEEE Geoscience and Remote Sensing Letters.

[7]  J. P. Fernández,et al.  Application of the normalized surface magnetic charge model to UXO discrimination in cases with overlapping signals , 2007 .

[8]  Kevin O'Neill,et al.  Use of standardized source sets for enhanced EMI classification of buried heterogeneous objects , 2004, SPIE Defense + Commercial Sensing.

[9]  R. Storn,et al.  Differential Evolution - A simple and efficient adaptive scheme for global optimization over continuous spaces , 2004 .

[10]  J.A. Kong,et al.  Quasi-magnetostatic solution for a conducting and permeable spheroid , 2000, IGARSS 2000. IEEE 2000 International Geoscience and Remote Sensing Symposium. Taking the Pulse of the Planet: The Role of Remote Sensing in Managing the Environment. Proceedings (Cat. No.00CH37120).

[11]  Kevin O'Neill,et al.  Combined differential evolution and surface magnetic charge model algorithm for discrimination of UXO from non-UXO items: simple and general inversions , 2005, SPIE Defense + Commercial Sensing.

[12]  Jin Au Kong,et al.  Broadband analytical magnetoquasistatic electromagnetic induction solution for a conducting and permeable spheroid , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[13]  Jin Au Kong,et al.  Magnetoquasistatic response of conducting and permeable prolate spheroid under axial excitation , 2001, IEEE Trans. Geosci. Remote. Sens..

[14]  Bruce Barrow,et al.  Model-based characterization of electromagnetic induction signatures obtained with the MTADS electromagnetic array , 2001, IEEE Trans. Geosci. Remote. Sens..

[15]  Thomas H. Bell,et al.  Simple phenomenological models for wideband frequency-domain electromagnetic induction , 2001, IEEE Trans. Geosci. Remote. Sens..

[16]  Irma Shamatava,et al.  Analysis of EMI scattering to support UXO discrimination: heterogeneous and multiple objects , 2003, SPIE Defense + Commercial Sensing.

[17]  D. Oldenburg,et al.  A Discrimination Algorithm for UXO Using Time Domain Electromagnetics , 2001 .

[18]  Thomas H. Bell,et al.  Subsurface discrimination using electromagnetic induction sensors , 2001, IEEE Trans. Geosci. Remote. Sens..