An Innovative Methodological Approach Based on Compressive Sensing for the Synthesis and Control of Antenna Arrays

In the framework of antenna array synthesis and control, this thesis focus on the development and analysis of techniques based on the Bayesian Compressive Sensing (BCS) for the design of sparse antenna arrays and for the estimation of the direction of arrival (DoA) of signals impinging on an antenna array. After formulating the sparse-array synthesis problem in a probabilistic fashion, the single-task BCS (ST-BCS) is applied to the synthesis of symmetrical antenna arrays with real weights. In order to deal with the synthesis of sparse arrays with complex weights, the multitask version of the BCS (MT-BCS) is employed to correlate the real and imaginary part of the resulting excitation distribution. Concerning the DoA estimation problem, starting from the observation that the signals impinging on the antenna array are sparse in the spatial domain, a single-snapshot ST-BCS -based technique is proposed. Moreover, the MT-BCS -based extension of this technique is introduced in order to enhance the quality of the estimations through the exploitation of the correlation among different snapshots. In the numerical validation, an exhaustive analysis has been performed to assess effectiveness, reliability, but also limitations of the proposed methodologies. Comparisons with state-of-the-art are reported and discussed, as well.