In high range-resolution (HRR) radar systems, the returns from a single target may fall in multiple
adjacent range bins which individually vary in amplitude. A target following this representation is
commonly referred to as an extended target and results in more information about the target. However,
extracting this information from the radar returns is challenging due to several complexities.
These complexities include the single dimensional nature of the radar measurements, complexities
associated with the scattering of electromagnetic waves, and complex environments in which radar
systems are required to operate. There are several applications of HRR radar systems which extract
target information with varying levels of success. A commonly used application is that of imaging
referred to as synthetic aperture radar (SAR) and inverse SAR (ISAR) imaging. These techniques
combine multiple single dimension measurements in order to obtain a single two dimensional image.
These techniques rely on rotational motion between the target and the radar occurring during the
collection of the single dimension measurements. In the case of ISAR, the radar is stationary while
motion is induced by the target.
There are several difficulties associated with the unknown motion of the target when standard Doppler
processing techniques are used to synthesise ISAR images. In this dissertation, a non-standard Dop-pler approach, based on Bayesian inference techniques, was considered to address the difficulties.
The target and observations were modelled with a non-linear state space model. Several different
Bayesian techniques were implemented to infer the hidden states of the model, which coincide with
the unknown characteristics of the target. A simulation platform was designed in order to analyse
the performance of the implemented techniques. The implemented techniques were capable of successfully
tracking a randomly generated target in a controlled environment. The influence of varying
several parameters, related to the characteristics of the target and the implemented techniques, was
explored. Finally, a comparison was made between standard Doppler processing and the Bayesian
Dissertation (MEng)--University of Pretoria, 2013.