Synthetic aperture radar (SAR) was first invented in the early 1950s as the remote surveillance
instruments to produce high resolution 2D images of the illuminated scene with weather-independent,
day-or-night performance. Compared to the Real Aperture Radar (RAR), SAR is
synthesising a large virtual aperture by moving a small antenna along the platform path. Typical
SAR imaging systems are designed with the basic assumption of a static scene, and moving targets
are widely known to induce displacements and defocusing in the formed images. While the
capabilities of detection, states estimation and imaging for moving targets with SAR are highly
desired in both civilian and military applications, the Ground Moving Target Indication (GMTI)
techniques can be integrated into SAR systems to realise these challenging missions. The state-of-the-
art SAR-based GMTI is often associated with multi-channel systems to improve the detection
capabilities compared to the single-channel ones. Motivated by the fact that the SAR imaging
is essentially solving an optimisation problem, we investigate the practicality to reformulate
the GMTI process into the optimisation form. Furthermore, the moving target sparsities and
underlying similarities between the conventional GMTI processing and sparse reconstruction
algorithms drive us to consider the compressed sensing theory in SAR/GMTI applications.
This thesis aims to establish an end-to-end SAR/GMTI processing framework regularised by
target sparsities based on multi-channel SAR models. We have explained the mathematical model
of the SAR system and its key properties in details. The common GMTI mechanism and basics
of the compressed sensing theory are also introduced in this thesis. The practical implementation
of the proposed framework is provided in this work. The developed model is capable of realising
various SAR/GMTI tasks including SAR image formation, moving target detection, target state
estimation and moving target imaging. We also consider two essential components, i.e. the data
pre-processing and elevation map, in this work. The effectiveness of the proposed framework is
demonstrated through both simulations and real data.
Given that our focus in this thesis is on the development of a complete sparsity-aided
SAR/GMTI framework, the contributions of this thesis can be summarised as follows. First, the
effects of SAR channel balancing techniques and elevation information in SAR/GMTI applications
are analysed in details. We have adapted these essential components to the developed framework
for data pre-processing, system specification estimation and better SAR/GMTI accuracies.
Although the purpose is on enhancing the proposed sparsity-based SAR/GMTI framework, the
exploitation of the DEM in other SAR/GMTI algorithms may be of independent interest.
Secondly, we have designed a novel sparsity-aided framework which integrates the
SAR/GMTI missions, i.e. SAR imaging, moving target and background decomposition, and
target state estimation, into optimisation problems. A practical implementation of the proposed
framework with a two stage process and theoretically/experimentally proven algorithms are
proposed in this work. The key novelty on utilising optimisations and target sparsities is explained
in details.
Finally, a practical algorithm for moving target imaging and state estimation is developed
to accurately estimate the full target parameters and form target images with relocation and
refocusing capabilities. Compared to the previous processing steps for practical applications, the
designed algorithm consistently relies on the exploitation of target sparsities which forms the final
processing stage of the whole pipeline. All the developed components contribute coherently to
establish a complete sparsity driven SAR/GMTI processing framework
