Nicola Anselmi

Compressive Sensing Imaging of Non-Sparse 2D Scatterers by a Total-Variation Approach Within the Born Approximation

The problem of imaging two-dimensional “non-sparse” scatterers with piecewise-constant contrast is solved through an innovative total-variation compressive sensing (TV-CS) technique. Under Born approximation (BA), the time-harmonic two-dimensional inverse scattering problem is reformulated into an equivalent Augmented Lagrangian one according to the TV-CS formulation. The resulting unconstrained functional is then minimized by means of a deterministic alternating direction algorithm. Selected results from a numerical assessment are discussed to give the interested reader some insights on the accuracy, the flexibility, the robustness, and the efficiency of the TV-CS inversion scheme also in comparison with available implementations of state-of-the-art inversion methods still formulated within the BA.

Tolerance Analysis of Antenna Arrays Through Interval Arithmetic

An analytical method based on Interval Analysis (IA) is proposed to predict the impact of the manufacturing tolerances of the excitation amplitudes on the radiated array pattern. By expressing the array factor according to the rules of the Interval Arithmetic, the radiation features of the linear array are described in terms of intervals whose bounds are analytically determined as functions of the nominal value and the tolerances of the array amplitudes. A set of representative numerical experiments dealing with different radiated beams and linear array sizes is reported and discussed to point out the features and potentials of the proposed approach.

Analysis of the Pattern Tolerances in Linear Arrays With Arbitrary Amplitude Errors

The analysis of the tolerances on the power pattern of an array of electromagnetic radiators having excitation amplitudes affected by random errors around the nominal values is addressed. Toward this end, an analytic strategy based on interval analysis is exploited to predict the bounds of the variations in the radiated power pattern in correspondence with difference models of the amplitude tolerances. Selected results are presented and discussed in a comparative fashion as well.

Synthesis of Multilayer WAIM Coatings for Planar-Phased Arrays Within the System-by-Design Framework

The synthesis of multilayer wide-angle impedance-matching (WAIM) structures for waveguide-fed planar phased array antennas is addressed. Under multifrequency constraints and assuming arbitrary array layouts, the synthesis problem is formulated within the system-by-design (SbD) framework. An iterative strategy is implemented by combining a fast EM modeling tool for the computation of the voltage reflection coefficient at the array surface and a solution-space sampling loop driven by the set of physical constraints describing both the synthesis objectives and the range boundaries for the degrees-of-freedom (DoFs) of the problem at hand. Thanks to the effectiveness and the reliability of such an integrated approach, it is possible to deal with multilayer multifrequency WAIM structures with uniaxially anisotropic permittivity and permeability tensors. Selected numerical results are presented to assess the potentialities and the reliability of the proposed approach also in comparison with state-of-the-art techniques.

Wavelet-Based Compressive Imaging of Sparse Targets

The application of the compressive sensing (CS) paradigm to retrieve non-single-pixels contrast profiles is discussed. By exploiting a wavelet representation to model complex scatterer distributions with sparse vectors of coefficients, an efficient Bayesian CS (BCS) strategy is adopted to solve the arising inverse scattering problem. A set of representative numerical examples is presented to illustrate the advantages and the limitations of the proposed approach also with respect to comparable state-of-the-art inversion methods.

Dealing With Uncertainties on Phase Weighting of Linear Antenna Arrays by Means of Interval-Based Tolerance Analysis

The analysis of the tolerances on the power patterns radiated by linear antenna arrays whose excitations are affected by phase errors is carried out by means of an approach based on the interval analysis (IA). The rules and the features of the arithmetic of intervals, already effectively applied to analyze the effects of errors on the amplification levels, are exploited here to derive a suitable set of analytical relationships predicting the maximum deviations (i.e., upper and lower bounds) with respect to the nominal power pattern once the phase errors are defined as belonging to intervals of values. Representative numerical examples are reported and discussed to assess potentialities and limitations of the IA-based approach. Toward this end, different configurations of the beamforming weights affording low-sidelobes and steered beam patterns are analyzed.

Interval Arithmetic for Pattern Tolerance Analysis of Parabolic Reflectors

An innovative analytic strategy for the estimation of the average pattern behavior of reflector antennas when surface deformations are present on the parabolic disc is proposed. Since measuring the surface deviations from a reference ideal shape is a complex task, the root-mean-square (rms) surface deformations are modeled as intervals and their impact on the power pattern as well as the sensitivity of the beam features is efficiently predicted by exploiting the rules of the interval arithmetic throughout interval analysis (IA). The result is the definition of closed-form relationships between the bounds (upper and lower values) of the radiated power pattern and the rms interval surface deformations. Uniformly and non-uniformly distributed perturbations of the ideal surface are analyzed to assess the effectiveness of proposed approach. Comparisons with state-of-the-art solutions are also carried out by varying the deformation thickness as well as the reflector geometry and illumination.

Multifrequency Particle Swarm Optimization for Enhanced Multiresolution GPR Microwave Imaging

An innovative inverse scattering (IS) technique for the simultaneous processing of multifrequency (MF) ground-penetrating radar (GPR) measurements is proposed. The nonlinear IS problem is solved by profitably integrating a customized MF version of the particle swarm optimizer (PSO) within the iterative multiscaling approach (IMSA) to jointly exploit the reduction of the ratio between unknowns and uncorrelated data with a pervasive exploration of the multidimensional search space for minimizing the probability that the solution is trapped into local minima corresponding to false solutions of the problem at hand. Both numerical and experimental test cases are reported to assess the reliability of the MF-IMSA-PSO method toward accurate GPR tomography as well as improvements with respect to the competitive state-of-the-art inversion approaches.

Optimal Synthesis of Robust Beamformer Weights Exploiting Interval Analysis and Convex Optimization

An approach for the optimal design of linear arrays in the presence of tolerances on the amplitude weights is presented. Starting from the knowledge of the maximum deviations from the nominal values of the amplitude coefficients of the beam-forming network (BFN), an analytical tool based on interval analysis (IA) and integrating a convex programming (CP) method is exploited to maximize the minimum peak value of the main beam of the radiated power pattern along the boresight direction, while fitting an arbitrary user-defined bound on the secondary lobes in a globally optimal fashion. In order to analyze the behavior of the proposed approach also assessing its effectiveness, a set of representative numerical examples regarding arrays of ideal and realistic radiating elements is reported where different sidelobe constraints, array configurations, and tolerance errors are considered.

Real-Time NDT-NDE Through an Innovative Adaptive Partial Least Squares SVR Inversion Approach

The real-time retrieval of the characteristics of a defect with eddy current testing in a nondestructive testing and evaluation framework is addressed. An innovative statistical learning approach is developed to deal with the inversion problem at hand in a computationally efficient way. More in detail, a feature extraction technique based on partial least squares (PLS) is profitably combined with a customized output space filling (OSF) adaptive sampling scheme for generating optimal training databases, while accurate and robust reconstructions are performed with a support vector regression (SVR) algorithm. A selected set of numerical and experimental results is reported to assess the effectiveness as well as the efficiency of the proposed PLS-OSF/SVR approach.