By Topic

Extraction of Landmine Features Using a Forward-Looking Ground-Penetrating Radar With MIMO Array

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Tian Jin ; Sch. of Electron. Sci. & Eng., Nat. Univ. of Defense Technol., Changsha, China ; Jun Lou ; Zhimin Zhou

A vehicle-mounted forward-looking ground-penetrating radar (GPR) with multiple-input and multiple-output (MIMO) array can obtain the high-resolution image of its front area to perform the standoff detection of landmines. The major challenge for the GPR landmine detection over wide areas is the very high false alarm rate when maintaining a high detection probability. In this paper, a novel feature extraction method is proposed to obtain the bistatic scattering information from the MIMO array image to discriminate landmines from clutter. To realize the goal, an imaging model of the MIMO array is firstly developed. Based on the imaging model, the bistatic scattering function of a suspected object is estimated from its MIMO array image using the space-wavenumber processing. Images of different incident angles and bistatic angles at some resonance frequencies are selected from the estimated bistatic scattering function to represent the scattering characteristics. In order to obtain the scale, rotation, and translation invariant feature vector, Hu moment invariants of the selected images are calculated to form the low-dimensional feature vector. The experimental results show that the proposed method can offer an efficient feature vector for the landmine discriminator to improve the detection performance.

Published in:

Geoscience and Remote Sensing, IEEE Transactions on  (Volume:50 ,  Issue: 10 )