Share Email Print

Proceedings Paper

Fusion of acoustic/seismic and GPR detection algorithms
Author(s): Paul D. Gader; Joseph N. Wilson; Tsaipei Wang; James M. Keller; Wen-Hsiung Lee; Roopnath Grandhi; Ali Koksal Hocaoglu; John McElroy
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

A variety of sensors have been investigated for the purpose of detecting buried landmines in outdoor environments. Mines with little or no metal are very difficult to detect with traditional mine detection systems. Ground Penetrating Radar (GPR) sensors have shown great promise in detecting low metal mines and can easily detect metal mines. Unfortunately, it can still be difficult to detect low-metal mines with GPR due to very low contrast between the mine and the surrounding medium. Acoustic-seismic systems were proposed by Sabatier and have also shown great promise in detecting low metal mines. There are now a wealth of references that discuss these systems and algorithms for processing data from these systems. Therefore, they will not be discussed in detail here. In fact, low-metal mines are easier to detect than metal mines with this acoustic-seismic systems. Low metal mines that are difficult for a GPR to detect can be quite easy to detect with acoustic-seismic systems. Sensor fusion with these sensors is of interest since together they can find a broader range of mines with relative ease. The algorithmic challenge is to determine a strategy for combining the multi-sensor information in a way that can increase the probability of detection without increasing the false alarm rate significantly. In this paper, we investigate fusion of information obtained from GPR and acoustic-seismic on real data measured from a mine lane containing three types of buried landmines and also areas containing no landmines. Algorithms are applied to data acquired from each sensor and confidence values are assigned to each location at which a measurement is made by each sensor. The GPR is used as a primary sensor. At each location at which the GPR algorithm declares an alarm, a modified likelihood-based approach is used to increase the GPR derived confidence if the likelihood that a mine is present, defined by the acoustic-based confidence, is larger than the likelihood that no mine is present. If the acoustic-derived confidence is very high, then a declaration is made even if there is no GPR declaration. The experiments were conducted using data acquired from the sensors at different times. The acoustic-seismic system collected data over a subset of the region at which the GPR collected data. Results are given only over those regions for which both sensors collected data.

Paper Details

Date Published: 11 September 2003
PDF: 9 pages
Proc. SPIE 5089, Detection and Remediation Technologies for Mines and Minelike Targets VIII, (11 September 2003);
Show Author Affiliations
Paul D. Gader, Univ. of Florida (United States)
Joseph N. Wilson, Univ. of Florida (United States)
Tsaipei Wang, Univ. of Missouri-Columbia (United States)
James M. Keller, Univ. of Missouri-Columbia (United States)
Wen-Hsiung Lee, Univ. of Florida (United States)
Roopnath Grandhi, Univ. of Florida (United States)
Ali Koksal Hocaoglu, Univ. of Florida (United States)
John McElroy, Univ. of Florida (United States)

Published in SPIE Proceedings Vol. 5089:
Detection and Remediation Technologies for Mines and Minelike Targets VIII
Russell S. Harmon; John H. Holloway Jr.; J. T. Broach, Editor(s)

© SPIE. Terms of Use
Back to Top
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?