Syntactic pattern recognition is being used to detect and classify non-metallic landmines in terms of their
range impedance discontinuity profile. This profile, extracted from the ground penetrating radar's return
signal, constitutes a high-range-resolution and unique description of the inner structure of a landmine. In
this paper, we discuss two preprocessing steps necessary to extract such a profile, namely, inverse filtering
(deconvolving) and binarization. We validate the use of an inverse filter to effectively decompose the observed
composite signal resulting from the different layers of dielectric materials of a landmine. It is demonstrated
that the transmitted radar waveform undergoing multiple reflections with different materials does not change
appreciably, and mainly depends on the transmit and receive processing chains of the particular radar
being used. Then, a new inversion approach for the inverse filter is presented based on the cumulative
contribution of the different frequency components to the original Fourier spectrum. We discuss the tradeoffs
and challenges involved in such a filter design. The purpose of the binarization scheme is to localize the
impedance discontinuities in range, by assigning a '1' to the peaks of the inverse filtered output, and '0' to
all other values. The paper is concluded with simulation results showing the effectiveness of the proposed
preprocessing technique.
Recently, there has been considerable interest in the development of robust, cost-effective and high performance
non-metallic landmine detection systems using ground penetrating radar (GPR). Many of the
available solutions try to discriminate landmines from clutter by extracting some form of statistical or geometrical
information from the raw GPR data, and oftentimes, it is difficult to assess the performance of such
systems without performing extensive field experiments. In our approach, a landmine is characterized by a
binary-valued string corresponding to its impedance discontinuity profile in the depth direction. This profile
can be detected very quickly utilizing syntactic pattern recognition. Such an approach is expected to be very
robust in terms of probability of detection (Pd) and low false alarm rates (FAR), since it exploits the inner
structure of a landmine. In this paper, we develop a method to calculate an upper bound on the FAR, which
is the probability of false alarm per unit area. First, we parameterize the number of possible mine patterns
in terms of the number of impedance discontinuities, dither and noise. Then, a combinatorial enumeration
technique is used to quantify the number of admissible strings. The upper bound on FAR is given as the
ratio of an upper bound on the number of possible mine pattern strings to the number of admissible strings
per unit area. The numerical results show that the upper bound is smaller than the FAR reported in the
literature for a wide range of parameter choices.
KEYWORDS: Land mines, General packet radio service, Electronic filtering, Signal to noise ratio, Antennas, Binary data, Radar, Dielectrics, Mining, Interfaces
A high range resolution ground penetrating radar signal is processed to convert the A-scan data into a binary valued
string in which a one represents the location of an impedance change and a zero otherwise. For non-metallic landmines it
has been shown that this pattern is unique and can be used to discriminate among landmines and clutter. The
discrimination method is based on regular languages which consist of the binarized sequences produced by various
landmines. Methods have been developed to automatically create language recognizers which not only recognize a
landmine's characteristic string, but also variations of those strings.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.