An optimisation framework for near field multifocusing on antenna arrays is proposed. Its aim is to obtain the feeding weights that must be applied to the elements of the array so that the antenna focuses on two or more targets simultaneously. The method is based on the definition of a proper cost function, which is minimised using an optimisation algorithm over an equivalent least squares problem. The success of the technique is noted in the fact that different array parameters can be obtained: magnitude and phase of the array elements or only phase synthesis. In order to show the efficiency of the method, several tests are carried out using different arrays and optimisation parameters. The simulation, manufacture and measurement of an 8 × 8 planar array able to focus on two different targets is also performed. Results show how the framework can be applied to general arrays and demanding requirements, so it may be easily extended to different synthesis problems besides multifocusing.

A novel Near-Field Focusing approach based on the use of artificial Neural Networks is proposed. It is able to provide the set of weights or feeding values that must be applied to the elements of an array so that the global radiation/reception pattern is focused on one or more predefined positions in the near environment. Due to the use of a properly trained Neural Network, it is able to work fast enough for real time applications, such as wireless energy and information transfer where moving devices may require quick adaptation of the radiated field distribution, and hence of the weights applied to the array. Moreover, the training procedure using examples generated with a convenient electromagnetic analysis tool allows taking into account both the radiation pattern of the elements of the array and the coupling effects between them.

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Neural networks and genetic algorithms are combined in a new hybrid technique able to perform synthesis tasks over passive-element antenna arrays, whose radiation pattern is defined by the coupling effects between the active and passive elements. The combination of both methods improves the results obtained using neural networks, also reducing the computation time required by genetic algorithms. Some significant results are presented.

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