Design, Modeling, and Validation of Curvature-Based Shape-Aware Flexible Phased Arrays

Abstract

Knowledge of the shape of deformed, mechanically flexible phased arrays plays an important role in the ability to maintain their beam pattern. This work presents the design, modeling, and experimental validation of such arrays with integrated curvature-based shape sensing. We analyze, over a large set of representative deformed shapes, the sensor density required for effective deformation sensing under measurement noise; develop and experimentally validate a physical model relating measured strain to local board curvature; and demonstrate an end-to-end design of a shape-aware, 6-GHz, eight-element array in conformed and free-hanging scenarios, achieving an average reconstruction accuracy of ~6% while recovering beam-steering performance at radii of curvature below 3.6 cm. The system utilizes a low-cost, eight-channel, 360$^{\circ}$ digitally controlled phase-shifter platform useful for a wide range of smart-antenna experiments. The shape is recovered from low-rate surface measurements, independently of RF conditions and without feedback, making the approach a general sensing modality applicable beyond the beam correction demonstrated here.