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We discuss some results and observations on applying syntactic pattern recognition (SPR) methodology for
landmine detection using impulse ground-penetrating radar (GPR). In the SPR approach, the GPR A-scans
are first converted into binary-valued strings by inverse filtering, followed by concavity detection to identify
the peaks and valleys representing the locations of impedance discontinuities in the return signal. During
the training phase, the characteristic binary strings for a particular landmine are found by looking at all
the exemplars of that mine and selecting the collection of strings that yield the best detection results on
these exemplars. These characteristic strings can be detected very efficiently using finite state machines
(FSMs). Finally, the FSM detections are clustered to assign confidence to each detection, and discard sparse
detections.
Provided that the impulse GPR provides enough resolution in range, the SPR method can be a robust
and high-speed solution for landmine detection and classification, because it aims to exploit the impedance
discontinuity profile of the target, which is a description of the internal material structure of the target and
little affected by external clutter. To evaluate the proposed methodology, the SPR scheme is applied to a set
of impulse GPR data taken at a government test site. We suggest that coherent frequency-agile radar may
be a better option for the SPR approach, since it addresses some of the drawbacks of a non-coherent impulse
GPR caused by internally non-coherent within-channel signals which necessitate non-coherent integration
and its attendant longer integration times, and non-coherent adjacent channels which severely limit the
ability to do spatial, or at a minimum, cross-range processing if the GPR is in a linear array antenna.
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