Beamforming and time reversal imaging for near-field electromagnetic localisation using planar antenna arrays
- Publication Type:
- Thesis
- Issue Date:
- 2011
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The localisation of radiating sources of electromagnetic waves in the near-field of a
receiver antenna array are of use in a vast range of applications, such as in microwave
imaging, wireless communications, RFID, real time localisation systems and remote
sensing etc. Localisation of targets embedded in a background dielectric medium, which is
usually the case in Radar, UWB imaging and remote sensing, can be done using the
scattered response received at the antennas. In this thesis, we investigate methods for
localisation of both near-field radiating as well as scattering sources of electromagnetic
waves.
For localisation of near-field radiating sources, planar antenna arrays such as
concentric circular ring array (CCRA), uniform rectangular array (URA), uniform circular
array (UCA) and elliptic array are employed. The thesis employs beamforming and
parameter estimation methods for localisation and proposes novel algorithms that are based
on standard Capon beamformer (SCB), subspace based superresolution algorithms
(MUSIC and ESPRIT) and maximum likelihood (ML) methods. Complex array geometries
can suffer from severe mutual coupling and are susceptible to array modelling errors.
These errors impair the performance of algorithms that are used for beamforming and
parameter estimation for localisation. To overcome the limitations of standard Capon
beamformer (SCB), a modified capon beamforming method is proposed to make SCB
robust against both array modelling error and mutual coupling effects. The proposed
method is applied with planar antenna arrays for localisation of near-field sources. Planar
arrays are also used with MUSIC and ESPRIT superreso lution algorithms for performance
investigation in a near-field source localisation. Here, to reduce the computational burden
of standard MUSIC and ESPRIT algorithms, a novel method to estimate the range using
the time-delay is proposed. Lastly, to overcome the performance limitations of
superresolution algorithms with planar arrays, the ML estimation is investigated for the
localisation of near-field sources using planar arrays. Since ML method cannot
automatically detect the number of sources, a novel method is proposed here for detecting
the number of sources. Finally, performance comparisons of all the methods under
investigation have been presented using computer simulations.
In order to localise targets embedded either in homogeneous or in heterogeneous
background medium, we employ time reversal (TR) techniques that localise based on the
received scattering responses from the embedded targets. We propose a novel beamspace-
TR technique that can achieve efficient focusing on targets embedded in both a
homogeneous and heterogeneous dielectric background media. It is shown that prior to
back propagation, applying beamspace processing to the TR operation in the receiving
mode helps achieve a reduced dimensional computation and achieves selective focusing.
We have also proposed beamspace-TR-MUSIC algorithm for improving the resolution of
standard TR-MUSIC algorithm. Performance of these techniques is investigated for
localising the target embedded in a clutter rich dielectric background where the dielectric
contrast between the target and the background medium is very low. We also propose to
extend the maximum likelihood based TR (TR-ML) to improve the focusing ability and to
help to localise dielectric targets embedded in a highly cluttered dielectric medium. To
prove the ability of the proposed algorithms, they are applied to the problem of UWB radar
imaging for the detection of early stage breast cancer. Computer simulations are used for
the investigation of the imaging performance of TR, beamspace-TR, TR-MUSIC,
beamspace-TR-MUSIC and TR-ML methods on a two-dimensional electromagnetic
heterogeneous dielectric scattering model of the breast.
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