| Autor: |
Abbasi, M.A.B, Akinsolu, Mobayode O., Liu, Bo, Yurduseven, O., Fusco, V. F., Imran, M. A |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
|
| ISSN: |
2045-2322 |
| Popis: |
This paper presents a millimeter-wave direction of arrival estimation (DoA) technique powered by dynamic aperture optimization. The frequency-diverse medium in this work is a lens-loaded oversized mmWave cavity that hosts quasi-random wave-chaotic radiation modes. The presence of the lens is shown to confine the radiation within the field of view and improve the gain of each radiation mode; hence, enhancing the accuracy of the DoA estimation. It is also shown, for the first time, that a lens loaded-cavity can be transformed into a lens-loaded dynamic aperture by introducing a mechanically controlled mode-mixing mechanism inside the cavity. This work also proposes a way of optimizing this lens-loaded dynamic aperture by exploiting the mode mixing mechanism governed by a machine learning-assisted evolutionary algorithm. The concept is verified by a series of extensive simulations of the dynamic aperture states obtained via the machine learning-assisted evolutionary optimization technique. The simulation results show a 25% improvement in the conditioning for the DoA estimation using the proposed technique. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
|
Full text from SpringerLink