Performance comparison of frequency domain quadrupole and dipole electromagnetic induction sensors in a landmine detection application

This work provides a performance comparison between two frequency-domain electromagnetic induction (EMI) sensors - one quadrupole and one dipole sensor for the detection of subsurface anti-personnel and anti-tank landmines. A summary of the physical differences between the two sensors and from those of other EMI sensors will be discussed. Previously we presented a performance analysis of the dipole sensor for a variety of detection algorithms over data collected at a government test facility indicating robust performance using the dipole sensor. The algorithms considered previously included an energy detector, matched subspace detector and a kNN probability density estimation approach over the features of a four parameter phenomenological model. The current sensor comparison will include, in addition to the previous detection methods, a Random Forests classification algorithm and utilize a larger training data set.

[1]  Waymond R. Scott Broadband electromagnetic induction sensor for detecting buried landmines , 2007, 2007 IEEE International Geoscience and Remote Sensing Symposium.

[2]  Tin Kam Ho,et al.  The Random Subspace Method for Constructing Decision Forests , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[3]  Thomas H. Bell,et al.  Discriminating capabilities of multifrequency EMI data , 2000 .

[4]  Louis L. Scharf,et al.  Matched subspace detectors , 1994, IEEE Trans. Signal Process..

[5]  Leslie M. Collins,et al.  A statistical approach to landmine detection using broadband electromagnetic induction data , 2002, IEEE Trans. Geosci. Remote. Sens..

[6]  S. K. Runcorn,et al.  Interpretation theory in applied geophysics , 1965 .

[7]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[8]  Leslie M. Collins,et al.  Performance of a four parameter model for modeling landmine signatures in frequency domain wideband electromagnetic induction detection systems , 2007, SPIE Defense + Commercial Sensing.

[9]  Kazuo Hattori,et al.  A new nearest-neighbor rule in the pattern classification problem , 1999, Pattern Recognit..

[10]  Shigeo Abe DrEng Pattern Classification , 2001, Springer London.

[11]  David G. Stork,et al.  Pattern Classification , 1973 .

[12]  L. Collins,et al.  Model-based statistical signal processing using electromagnetic induction data for landmine detection and classification , 2001, Proceedings of the 11th IEEE Signal Processing Workshop on Statistical Signal Processing (Cat. No.01TH8563).

[13]  B. Krauskopf,et al.  Proc of SPIE , 2003 .

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

[15]  Lawrence Carin,et al.  Wideband electromagnetic induction for metal-target identification: theory, measurement, and signal processing , 1998, Defense, Security, and Sensing.

[16]  Waymond R. Scott,et al.  New cancellation technique for electromagnetic induction sensors , 2005, SPIE Defense + Commercial Sensing.

[17]  Cisr Jmu Adopt-A-Minefield , 2000 .

[18]  Leslie M. Collins,et al.  Performance of matched subspace detectors and support vector machines for induction-based land mine detection , 2002, SPIE Defense + Commercial Sensing.

[19]  Peter Acerbo Torrione,et al.  A COMPARISON OF STATISTICAL ALGORITHMS FOR LANDMINE DETECTION , 2002 .

[20]  Leslie M. Collins,et al.  Classification of landmine-like metal targets using wideband electromagnetic induction , 2000, IEEE Trans. Geosci. Remote. Sens..

[21]  J. R. Lockwood,et al.  Alternatives for landmine detection , 2003 .

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

[23]  Leslie M. Collins,et al.  Performance Comparison of Automated Induction-Based Algorithms for Landmine Detection in a Blind Field Test , 2004 .