In this thesis, the use of array processing techniques applied to Single Input
Multiple Output (SIMO) SAR systems with enhanced capabilities is investigated.
In Single Input Single Output (SISO) SAR systems there is a high resolution,
wide swath contradiction, whereby it is not possible to increase both cross-range
resolution and the imaged swath width simultaneously. To overcome this, a
novel beamformer for SAR systems in the cross-range direction is proposed. In
particular, this beamformer is a superresolution beamformer capable of forming
wide nulls using subspace based approaches.
SIMO SAR systems also give rise to additional sets of received data, which
includes geometrical information about the SAR and target environment, and
can be used for enhanced target parameter estimation. In particular, this thesis
looks at round trip delay, joint azimuth and elevation angle, and relative target
power estimation. For round trip delay estimation, the use of the traditional
matched filter with subspace partitioning is proposed. Then by using a joint
2D Multiple Signal Classification (MUSIC) algorithm, joint Direction of Arrival
(DOA) estimation can be achieved. Both the use of range lines of raw SAR
data and the use of a Region of Interest (ROI) of a SAR image are investigated.
However in terms of imaging, MUSIC is not well-suited for SAR, due to its
target response not corresponding to the target's true power return. Therefore a
joint DOA and target power estimation algorithm is proposed to overcome this
limitation.
These algorithms provide the framework for the development of three processing
techniques. These allow sidelobe suppression in the slant range direction, along
with the reconstruction of undersampled data and region enhancement using
MUSIC with power preservation.