Paolo Rocca

Differential Evolution as Applied to Electromagnetics

In electromagnetics, optimization problems generally require high computational resources and involve a large number of unknowns. They are usually characterized by non-convex functionals and continuous spaces suitable for strategies based on Differential Evolution (DE). In such a framework, this paper is aimed at presenting an overview of Differential Evolution-based approaches used in electromagnetics, pointing out novelties and customizations with respect to other fields of application. Starting from a general description of the evolutionary mechanism of Differential Evolution, Differential Evolution-based techniques for electromagnetic optimization are presented. Some hints on the convergence properties and the sensitivity to control parameters are also given. Finally, a comprehensive coverage of different Differential Evolution formulations in solving optimization problems in the area of computational electromagnetics is presented, focusing on antenna synthesis and inverse scattering.

Handling Sideband Radiations in Time-Modulated Arrays Through Particle Swarm Optimization

The minimization of the power losses in time-modulated arrays is addressed by means of a suitable strategy based on particle swarm optimization. By properly modifying the modulation sequence, the method is aimed at reducing the amount of wasted power, analytically computed through a very effective closed-form relationship, while constraining the radiation pattern at the carrier frequency below a fixed sidelobe level. Representative results are reported and compared with previously published solutions to assess the effectiveness of the proposed approach.

A Bayesian-Compressive-Sampling-Based Inversion for Imaging Sparse Scatterers

In this paper, a new approach based on the Bayesian compressive sampling (BCS ) and within the contrast source formulation of an inverse scattering problem is proposed for imaging sparse scatterers. By enforcing a probabilistic hierarchical prior as a sparsity regularization constraint, the problem is solved by means of a fast relevance vector machine. The effectiveness and robustness of the BCS-based approach are assessed through a set of numerical experiments concerned with various scatterer configurations and different noisy conditions.

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.

Three-Dimensional Microwave Imaging Problems Solved Through an Efficient Multiscaling Particle Swarm Optimization

An enhanced multistep strategy based on a multiresolution particle swarm optimizer is proposed for 3-D microwave imaging. The aim of such an integration is to improve the convergence capabilities of the approach and to reduce the dimension of the search space and the computational burden of the optimization strategy, thanks to a constrained control of the particle velocities adaptively determined. This favors the exploitation of the global search capabilities of the particle swarms also in the framework of large-scale 3-D inverse scattering problems. The proposed technique is assessed by considering numerical tests concerned with single and multiple 3-D targets. The results of an experimental testing are also discussed.

An Innovative Approach Based on a Tree-Searching Algorithm for the Optimal Matching of Independently Optimum Sum and Difference Excitations

An innovative approach for the optimal matching of independently optimum sum and difference patterns through sub-arrayed monopulse linear arrays is presented. By exploiting the relationship between the independently optimal sum and difference excitations, the set of possible solutions is considerably reduced and the synthesis problem is recast as the search of the best solution in a noncomplete binary tree. Towards this end, a fast resolution algorithm that exploits the presence of elements more suitable to change subarray membership is presented. The results of a set of numerical experiments are reported in order to validate the proposed approach pointing out its effectiveness also in comparison with state-of-the-art optimal matching techniques.

Wireless Architectures for Heterogeneous Sensing in Smart Home Applications: Concepts and Real Implementation

Application of wireless technologies in the smart home is dealt with by pointing out advantages and limitations of available approaches for the solution of heterogeneous and coexisting problems related to the distributed monitoring of the home and the inhabitants. Some hot challenges facing the exploitation of noninvasive wireless devices for user behavior monitoring are then addressed and the application fields of smart power management and elderly people monitoring are chosen as representative cases where the estimation of user activities improves the potential of location-aware services in the smart home. The problem of user localization is considered with great care to minimize the invasiveness of the monitoring system. Wireless architectures are reviewed and discussed as flexible and transparent tools toward the paradigm of a totally automatic/autonomic environment. With respect to available state-of-the-art solutions, our proposed architecture is based also on existing wireless devices and exploits, in an opportunistic way, the characteristics of wireless signals to estimate the presence, the movements, and the behaviors of inhabitants, reducing the system complexity and costs. Selected and representative examples from real implementations are presented to give some insight on state-of-the-art solutions also envisaging possible future trends.

Harmonic Beamforming in Time-Modulated Linear Arrays

In this paper, the synthesis of simultaneous multibeams through time-modulated linear arrays is studied. Unlike classical phased arrays where the antenna aperture is usually shared to generate multiple beams, the periodic on-off sequences controlling the static excitations are properly defined by means of an optimization strategy based on the Particle Swarm algorithm to afford desired multiple patterns at harmonic frequencies to make practical application of these harmonic beams which are typically regarded as an undesirable effect in time-modulated arrays. The synthesis of simultaneous broadside sum and difference patterns, flat-top and narrow beam patterns, and steered multibeams is enabled as assessed by a set of selected results reported and discussed to show the potentialities of the proposed method. Comparisons with previously published results are reported, as well.

Electromagnetic passive localization and tracking of moving targets in a WSN-infrastructured environment

In this paper, an innovative strategy for the passive localization of transceiver-free objects is presented. The localization is yielded by processing the received signal strength data measured in an infrastructured environment. The problem is reformulated in terms of an inverse source one, where the probability map of the presence of an equivalent source modeling the moving target is looked for. Toward this end, a customized classification procedure based on a support vector machine is exploited. Selected, but representative, experimental results are reported to assess the feasibility of the proposed approach and to show the potentialities and applicability of this passive and unsupervised technique. “(c) Institute of Physics”

Reliable Diagnosis of Large Linear Arrays—A Bayesian Compressive Sensing Approach

An innovative array diagnosis technique based on a compressive-sensing (CS) paradigm is introduced in the case of linear arrangements. Besides detecting the faulty elements, the approach is able to provide the degree of reliability of such an estimation. Starting from the measured samples of the far-field pattern, the array diagnosis problem is formulated in a Bayesian framework and it is successively solved with a fast relevance vector machine (RVM). The arising Bayesian compressive sensing (BCS) approach is numerically validated through a set of representative examples aimed at providing suitable users guidelines as well as some insights on the method features and potentialities.