Data-derived SEA for Time Domain EMI Sensing of UXO
Electromagnetic induction (EMI) is a prominent technique in Unexploded Ordnance (UXO) deffection and discrimination research. Existing idealized forward models for the EMI response can be defeated by both the material and geometrical heterogeneity of realistic UXO. We have developed a new, physically complete modeling system referred to as the Standardized Excitations Approach (SEA). The SEA accounts for all the effects from these heterogeneities including their interactions within the object, and is applicable in both the near and far fields. According to the SEA, the excitation field is decomposed into fundamental modes, and the response of a given target to each fundamental mode (denoted as a fundamental solution) is obtained beforehand and saved in a library. In this way, the target response to an arbitrary excitation field can be calculated via a simple superposition of these fundamental solutions. The model parameters (I.e., the fundamental solutions) of a given object are extracted from a sufficiently detailed set of measurement data. These parameters will be specific to each EMI instrument. The parameter extraction process was developed previously for the frequency domain using the GEM-3 EMI instrument. In this paper, we apply this SEA to time domain using the EM-63 instrument as an example. The receiver coil of the EM63 is a 0.5m by 0.5m square loop and can not be approximated by a point receiver. Therefore, in the model, the data is interpreted as the integration of the secondary field over the receiver loop. The objects we consider are all Body of Revolution (BOR) type objects. We exploit the fact that the calculated SEA model parameters also exhibit specific behavior because the target is a BOR Thus, the algorithm is improved by enforcing symmetric properties and zero total magnetic charge, which makes the algorithm more robust and more efficient. Preliminary results show that this approach works well for this time domain EMI instrument. After optimization, this model may be fast enough for implementation in inversion processing algorithms.
K. Sun K ONeill B. E. Barrowes F. Shubitidze I. Shamatava J. P. Fernández K. D. Paulsen
Thayer School of Engineering, Dartmouth College, Hanover NH, 03755, USA Thayer School of Engineering, Dartmouth College, Hanover NH, 03755, USA USA ERDC Cold Regions Resear USA ERDC Cold Regions Research and Engineering Laboratory 72 Lyme Road, Hanover NH, 03755, USA
国际会议
Progress in Electromagnetics Research Symposium 2007(2007年电磁学研究新进展学术研讨会)(PIERS 2007)
北京
英文
330-333
2007-03-26（万方平台首次上网日期，不代表论文的发表时间）