|dc.description.abstract||The research work on radar detection has been widely investigated during the
last decades. For this, several techniques have been developed to analyze and
improve radar detection.
However, the difficulty that arises in radar detection is to find an algorithm which adapts
to a variety of environments encountered in practice. For this, it is always necessary to
develop novel methods.
An important concept which is in conjunction with the geometrical properties of an
object is the fractal geometry. This geometry, which describes well the complex and
irregular objects with its important parameter “the fractal dimension”, measures the
degree of complexity of the structure considered and was used in radar detection.
As part of our thesis, we propose a new radar detector based on the use of the fractal
dimension, estimated by the method of box counting and adapted to all types of clutter
in order to achieve the detection of radar signal in sea and ground clutter for synthetic
and real data.
Since its appearance, the radar imagery was subject to many studies, as well on the
level of acquisition as the image processing rebuilt in order to improve the quality of
information obtained. One of the advanced imaging radar technique is synthetic
aperture radar (SAR) with its two types of configurations monostatic and bistatic.
The process of generating a SAR image is undertaken via the use of signal processing
techniques to form the image from raw data. Indeed, multiple image formation
processes have been developed for monostatic SAR.
In this work, we use three algorithms for generating the bistatic SAR images: Matched
Filtering Algorithm (MFA), Back Projection Algorithm (BPA) and Polar Format
Algorithm (PFA). We study the performance of these algorithms on two types of clutter;
K and Weibull (real and complex).||fr_FR