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This paper presents a novel approach to the localization of moving targets in a complex environment based on the measurement of the perturbations induced by the target presence on an independently‐generated time‐varying electromagnetic field. Field perturbations are measured via a set of sensors deployed over the domain of interest and used to detect and track a possible target by resorting to a particle Bernoulli filter (PBF). To comply with real‐time operation, the PBF works along with an artificial neural network (ANN) model of the environment trained offline via finite elements (FEs). The performance of the proposed algorithm is assessed via simulation experiments. [ABSTRACT FROM AUTHOR]

A multisegment artificial neural network (ANN) is proposed as an interpolation technique for the evaluation of the electromagnetic field diffracted at the edge of anisotropic impedance wedges under plane wave illumination at oblique incidence. Multisegmentation is needed as the high-frequency wedge diffracted field is characterized by a number of discontinuities at the shadow boundaries of the geometrical optics and surface wave fields. The proposed approach is applied, as a test case, to the problem of an anisotropic impedance right-angled wedge illuminated by a skewly incident plane wave. Some exact analytical solutions valid for specific surface impedance tensors are used to obtain numerical data for the ANN training phase as well as to show the interpolation capabilities of the implemented ANN. Nevertheless, the proposed ANN structure is general and can be trained with data obtained from other available solutions (analytical, perturbative, numerical) valid for more general wedge configurations, eventually leading to a single software tool encompassing all of them and providing accurate approximations of the wedge diffracted field in a relatively short time, comparable to that of a closed form analytical solution. [ABSTRACT FROM AUTHOR]

This paper deals with many different neural network architectures that have been introduced to simulate the electromagnetic behavior of complex microwave devices and antennas. Many issues linked to the peculiarities of such devices are addressed. Furthermore, the inverse problem of designing a microwave device, once the required characteristics are given, is also developed by using a neural network approach. © 2002 John Wiley & Sons, Inc. Int J RF and Microwave CAE 12: 90–97, 2002. [ABSTRACT FROM AUTHOR]

Presents a study which examined the artificial neural network approach for the analysis and synthesis of profiled circular corrugated horn. Geometrical aspects of the profiled corrugated circular horns; Analysis and synthesis procedure; Conclusion.

In many filtering applications, direct-coupled cavity filters are often used for their symmetry and handling easiness. In this paper, a method is described for the design of these filtering devices by using an artificial neural network (ANN). Starting from design requirements, the procedure discussed herein directly determines the geometric dimensions of the filtering device with good accuracy and very short processing time. ©1999 John Wiley & Sons, Inc. Int J RF and Microwave CAE 9: 287–296, 1999. [ABSTRACT FROM AUTHOR]

A straightforward method for the design of microwave filtering devices by using neural networks is proposed. Starting from the filter requirements, this technique is able to perform the synthesis of microwave filters with good accuracy and high computational efficiency. The neural-network training set is generated by applying the finite-element (FE) approach. © 1998 John Wiley & Sons, Inc. Microwave Opt Technol Lett 19: 36–38, 1998. [ABSTRACT FROM AUTHOR]