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Reconfigurable Array Control Via Convolutional Neural Networks, Garrett A. Harris
Reconfigurable Array Control Via Convolutional Neural Networks, Garrett A. Harris
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A method for the beam forming control of an array of reconfigurable antennas is presented. The method consists of using two parallel convolutional neural networks (CNNs) to analyze a desired radiation pattern image, or mask, and provide a suggestion for the reconfigurable element state, array shape, and steering weights necessary to obtain the radiation pattern. This research compares beam forming systems designed for three distinct element types: a patch antenna, a reconfigurable square spiral antenna restricted to a single reconfigurable state, and the fully reconfigurable square spiral. The parametric sweeps for the design of the CNNs are presented along with …
Reconfigurable Antenna Array Using The Pin-Diode-Switched Printed Square Spiral Element, Corey M. Stamper
Reconfigurable Antenna Array Using The Pin-Diode-Switched Printed Square Spiral Element, Corey M. Stamper
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A dual-band array of reconfigurable elements is modeled and simulated. The array consists of four single square spiral microstrip antennas that are corporately fed and are used as a unit cell. The single square spiral uses two PIN diodes as switching elements to achieve reconfiguration. The array has four switch states that can operate over four discrete frequencies in the S and C bands. The unit cell radiation pattern is used with the pattern multiplication model to quickly simulate the array pattern of larger arrays. A 3 x 3 array of the unit cells is modeled with only the center …