Beamforming and time reversal imaging for near-field electromagnetic localisation using planar antenna arrays

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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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