Excitation Diversity in Adaptively Thinned Arrays for Microwave Sensing Applications

Thinned arrays are a class of non-uniform arrays in which the magnitudes of the excitation coefficients usually take on binary values. They are obtained by removing or connecting to matched loads the elements of a filled array, such that the final combination of active elements resembles that of a reference unequally-excited filled array. However, the advantage of reducing the complexity of the feeding network can lead, in some applications (such as in microwave sensing), to an unacceptable discrepancy between the actual radiation pattern and the desired one, especially for small to medium-sized arrays. In this work, an excitation diversity technique is included in thinned arrays design to overcome the above drawback. Data distributions achieved with the above approach are averaged to obtain potential high-quality final images. Moreover, the proposed methodology can be easily implemented in real-time adaptive arrays. The reported numerical results successfully prove the suitability of the proposed diversity technique, to be usefully applied for microwave sensing applications.