Abstract:
In this Master’s thesis, we deal with the improvement of the performance of the CFAR (Constant
False Alarm Rate) detection of a Swerling I type target embedded in an inhomogeneous Weibull
or Log-normal clutter with unknown parameters. We assume that the reference window may
contain a clutter edge, with an unknown position, partitioning the reference window into two sets
of IID (independent and identically distributed) Weibull or Log-normal samples each but with
different powers. In doing this, we analyze and compare two techniques of automatic localization
of a clutter edge position [1-3] whose goal is the automatic censoring of the unwanted samples of
either smaller or greater power. To this end, in the first technique [1], a logarithmic amplifier is
necessarily introduced; i.e., the Weibull and Log-normal distributions are reduced, respectively,
to a Gumbel and Normal distributions. In the second, however, technique found in [2, 3], the
samples should first be sorted in an ascending order. Finally, to estimate the clutter level and
decide the presence or the absence of the primary target, the selected homogeneous set yielded
by each technique is incorporated in one of the suitable CFAR detectors found in the radar
literature. More precisely, the CFAR detector should perform two tasks (algorithms); censoring
and CFAR detection. Indeed, the virtue of the automatic censoring detector is the considerable
improvement of detection performance over the corresponding detector, i.e., fixed point
censoring detector. To assess the efficiency of the censoring and detection of each improved
detector, we compare it in different scenarios, through Monte Carlo simulations, with its
corresponding fixed point censoring detector. The obtained results show that both exhibit the
same performance in a homogeneous clutter; however the former significantly outperforms the
latter in the presence of a clutter edge within the reference window.
