Matteo Carlin

Complex-Weight Sparse Linear Array Synthesis by Bayesian Compressive Sampling

An innovative method for the synthesis of maximally sparse linear arrays matching arbitrary reference patterns is proposed. In the framework of sparseness constrained optimization, the approach exploits the multi-task (MT) Bayesian compressive sensing (BCS) theory to enable the design of complex non-Hermitian layouts with arbitrary radiation and geometrical constraints. By casting the pattern matching problem into a probabilistic formulation, a Relevance-Vector-Machine (RVM) technique is used as solution tool. The numerical assessment points out the advances of the proposed implementation over the extension to complex patterns of and it gives some indications about the reliability, flexibility, and numerical efficiency of the MT-BCS approach also in comparison with state-of-the-art sparse-arrays synthesis methods.

Directions-of-Arrival Estimation Through Bayesian Compressive Sensing Strategies

The estimation of the directions of arrival (DoAs) of narrow-band signals impinging on a linear antenna array is addressed within the Bayesian compressive sensing (BCS) framework. Unlike several state-of-the-art approaches, the voltages at the output of the receiving sensors are directly used to determine the DoAs of the signals thus avoiding the computation of the correlation matrix. Towards this end, the estimation problem is properly formulated to enforce the sparsity of the solution in the linear relationships between output voltages (i.e., the problem data) and the unknown DoAs. Customized implementations exploiting the measurements collected at a unique time instant (single-snapshot) and multiple time instants (multiple-snapshots) are presented and discussed. The effectiveness of the proposed approaches is assessed through an extensive numerical analysis addressing different scenarios, signal configurations, and noise conditions. Comparisons with state-of-the-art methods are reported, as well.

Bayesian compressive sensing as applied to directions-of-arrival estimation in planar arrays

The Bayesian compressive sensing (BCS) is applied to estimate the directions of arrival (DoAs) of narrow-band electromagnetic signals impinging on planar antenna arrangements. Starting from the measurement of the voltages induced at the output of the array elements, the performance of the BCS-based approach is evaluated when data are acquired at a single time instant and at consecutive time instants, respectively. Different signal configurations, planar array geometries, and noise conditions are taken into account, as well.

Hybrid BCS-Deterministic Approach for Sparse Concentric Ring Isophoric Arrays

An innovative methodology for the synthesis of sparse concentric-ring isophoric arrays matching a user-defined reference pattern is proposed. The synthesis problem is recast as a two-step approach firstly aimed at (a) determining the sparsest auxiliary layout of concentric current rings matching the reference pattern through a Bayesian compressive sensing method and successively (b) yielding the concentric-ring isophoric array by means of a density tapering technique. A selected set of numerical experiments is presented to provide some insights about the effectiveness, the numerical efficiency, and the flexibility of the proposed hybrid method.

On the Robustness to Element Failures of Linear ADS-Thinned Arrays

The robustness of the analytical thinning based on almost difference sets (ADSs) is analyzed when faulty elements are present in linear arrays. Peak sidelobe level (PSL) statistics of the damaged layouts are numerically inferred from representative simulations dealing with different aperture sizes and element failure rates.

An SbD-QCTO Approach to the Synthesis of Isotropic Metamaterial Lenses

Within the transformation electromagnetics (TE) framework, an innovative approach, based on the System-by-Design paradigm, is proposed for the synthesis of isotropic nonmagnetic metamaterial lenses. Selected numerical results, concerned with an application of the SbD-QCTO approach, are reported to give some insights on its advantages and current limitations in terms of computational efficiency, effectiveness, and flexibility.

MICROWAVE IMAGING WITHIN THE INTERVAL ANALYSIS FRAMEWORK

An approach based on the use of the arithmetic of intervals and Interval Analysis for the solution of inverse scattering problems is presented and assessed. By exploiting the property of the Interval Analysis to flnd the global minimum of a functional in a n-dimensional space, the proposed approach adopts a branch and bound process to discard the regions of the solutions space not containing the global solution, while keeping those where a feasible solution is expected until a suitable converge criterion is reached. A representative set of results concerned with the reconstruction of circular dielectric objects within the flrst-order Born approximation are reported and discussed to show potentialities and current limitations of the proposed approach.

Interval Analysis as applied to inverse scattering

An innovative strategy based on Interval Analysis (IA) is presented to the solution of inverse scattering problems. First, the inverse problem is recast to an optimization one through the definition of a suitable function to be minimized which evaluates the misfit between the measured and the reconstructed field data according to the arithmetic of intervals. Then, IA is used to check n-dimensional interval portion of the solution space and verify whether the global minimum is either present or no. The intervals which are not discarded are iteratively sub-divided and tested until suitable termination conditions are achieved. Representative results are presented and discussed to show the potentialities of Interval Analysis as applied to inverse problems in electromagnetics.

Evolutionary strategies for advanced array optimization

Evolutionary algorithms (EAs) have had a widespread diffusion and have demonstrated to be very effective techniques for the design of antenna arrays. However, the main limitations are the high computational burden and low convergence speed which stimulated researchers towards the development of even more effective approaches or the definition of suitable optimization frameworks. Two innovative EA-based method for the synthesis of large thinned arrays and of compromise sum-difference sub-arrayed antennas are presented and discussed in this work where the flexibility and performance of EAs have been exploited to obtain effective and efficient designs.

Optimized synthesis of wave-manipulating devices within the system-by-design paradigm

An innovative approach to the synthesis of wave manipulating devices is proposed in this work within the framework of the System-by-Design (SbD) paradigm. The approach is based on the decomposition of the overall synthesis problem in terms of the combination of a set of functional blocks, each one performing a “simple” task. The design blocks (each one aimed at the analysis or synthesis of a specific sub-part of the wave manipulating device of interest) are then combined in a SbD loop to yield the final design procedure. Representative numerical examples are reported concerning the design of different wave manipulating devices to assess the features and potentialities of the proposed SbD approach.