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|Title:||Novel Techniques and Architectures for Adaptive Beamforming|
|Authors:||Ho, Van Thua|
|Keywords:||Electrical and Electronics;Electrical and Electronics|
|Abstract:||<p>Recent progress in VLSI technology has created a major impact on digital signal processing, including array signal processing. Proposals have been made for using high throughput processors for digital adaptive beamforming in radar and communications systems applications. In this thesis, novel techniques and architectures for adaptive beamforming will be developed and presented. These are typified by the development of adaptive beamforming algorithms for planar arrays and by a self-calibration algorithm for antenna arrays. The emphasis however will be placed on modern adaptive beamforming techniques in which the adaptation is carried out by means of a triangular systolic array processor performing the QH decomposition.</p> <p>Adaptive beamforming algorithms for a planar array or two-dimensional (2-D) adaptive beamforming algorithms, which are typified by the 2-D least-mean-squares (LMS) algorithm and 2-D Howells-Applebaum algorithm, are derived and presented. The concept of 2-D eigenbeams will be given to demonstrate the performance of the 2-D adaptive beamforming techniques. As well, the 2-D adaptive beamforming problem will be formulated in terms of the 1-D case with operation taking place along rows and columns of a planar array. The adaptive processor is then implemented by using a manifold of the least-squares triarray processors, which in the limit takes the form of a 3-D systolic array. It will be shown that the structure is capable of performing adaptation along the rows and columns of the 2-D array simultaneously.</p> <p>One of the major challenges that face workers in array processing is overcoming the degradation in the output of the high performance algorithms due to errors in the calibration of the array. A new self-calibration technique for solving this difficult problem will be derived and presented herein. The algorithm is based on the use of iteration - whereby the calibration coefficients are refined through repetitive imposition of the calibration procedure. Its derivation is based on the eigen-based method and the least-squares norm minimization. It will be shown that the algorithm is capable of automatical estimating the angles-of-arrival (AOA) of the received signals and calibrating the array with a minimum phase and gain errors. Results obtained by using both simulation and measurement data will be given. In the case of the experimental results, the measurement setup is subjected to multipath scenarios.</p>|
|Appears in Collections:||Open Access Dissertations and Theses|
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