Impact of Origami-Inspired and Pattern Reconfigurable Antennas on Direction-of-Arrival Estimation

Abstract

Reconfigurable techniques have been studied extensively for their capabilities of transforming an antenna’s pattern, polarization, and frequency of operation. The dynamic nature of reconfigurable antennas has played a pivotal role in the advancement of Direction Finding. Many techniques have been developed to manipulate the antenna’s characteristics to optimize the performance of the existing system. Researchers have continued to evolve the complexity of the antenna systems and signal processing algorithms to meet the growing demand for emitter localization. Recently, origami has increased the complexity of the design space of reconfiguration. The field has brought forth a bevy of antenna systems that leverage the morphing topology to reconfigure an antenna’s performance. The design space of geometrically-morphable origami-inspired arrays is very complex and has not been fully evaluated for its capabilities in Direction Finding. Therefore, a Miura-ori-inspired patch antenna array is designed on Ansys HFSS to investigate the complex design space and to understand how the physical reconfigurability affects the performance of an array. Then, a methodology is developed in python to assess the performance trade-offs when using complex reconfigurable antennas in a dynamic array environment in the context of a subspace directional-of-arrival technique. The complex reconfigurable antenna used in this work is capable of obtaining four unique pattern states using parasitic elements and PIN diodes. The impact of realistic antennas with pattern/directional-reconfigurable properties is evaluated in uniform arrays, non-uniform arrays, and morphing arrays. The dynamic array environment of the Miura-Ori patch antenna array is emulated using the inter-element spacing of the origami lattice. These two reconfiguration techniques are studied to determine their influence on DOA estimation by analyzing the root mean squared error using a Monte Carlo method. The estimation is performed using the Multiple Signal Classification (MUSIC) algorithm because of its ability to handle arbitrary arrays. Additionally, an iterative-MUSIC technique is evaluated for its ability to mitigate aliasing in a morphing array. Then, an unstructured thinning procedure is assessed for its ability to provide the elements with a non-uniform spacing that can mitigate aliasing and produce an accurate estimation. The impact of the active element density is determined by systematically decreasing the percentage of active elements in the unstructured thinned array. This dissertation investigates the cumulative impact of reconfigurable antennas in a morphing array topology and demonstrates that the combination of pattern/directionally-reconfigurable, unstructured thinning, and origami-inspired antenna arrays can be used to provide enhanced performance when compared to fixed arrays with static antennas.