Ground Penetrating Radar(GPR) is one of a number
of technologies that have been used to improve landmine
detection efficiency. The clutter environment within the first
few cm of the soil where landmines are buried, exhibits strong
reflections with highly non-stationary statistics. An antipersonnel
mine(AP) can have a diameter as low as 2cm whereas many
soils have very high attenuation frequencies above 3GHZ. The
landmine detection problem can be solved by carrying out system
level analysis of the issues involved to synthesise an image
which people can readily understand. The SIMCA (’SIMulated
Correlation Algorithm’) is a technique that carries out correlation
between the actual GPR trace that is recorded at the field and the
ideal trace which is obtained by carrying out GPR simulation.
The SIMCA algorithm firstly calculates by forward modelling a
synthetic point spread function of the GPR by using the design
parameters of the radar and soil properties to carry out radar
simulation. This allows the derivation of the correlation kernel.
The SIMCA algorithm then filters these unwanted components
or clutter from the signal to enhance landmine detection. The
clutter removed GPR B scan is then correlated with the kernel
using the Pearson correlation coefficient. This results in a image
which emphasises the target features and allows the detection of
the target by looking at the brightest spots. Raising of the image
to an odd power >2 enhances the target/background separation.
To validate the algorithm, the length of the target in some cases
and the diameter of the target in other cases, along with the
burial depth obtained by the SIMCA system are compared with
the actual values used during the experiments for the burial depth
and those of the dimensions of the actual target. Because, due
to the security intelligence involved with landmine detection and
most authors work in collaboration with the national government
military programs, a database of landmine signatures is not
existant and the authors are also not able to publish fully their
algorithms. As a result, in this study we have compared some of
the cleaned images from other studies with the images obtained
by our method, and I am sure the reader would agree that our
algorithm produces a much clearer interpretable image
