Machine Learning‑assisted Lens‑loaded Cavity Response Optimization for Improved Direction‑of‑arrival estimation
Abbasi, M.A.B, Akinsolu, Mobayode O., Liu, Bo, Yurduseven, O., Fusco, V. F. and Imran, M. A (2022) Machine Learning‑assisted Lens‑loaded Cavity Response Optimization for Improved Direction‑of‑arrival estimation. Scientific Reports, 12 (8511). pp. 1-13. ISSN 2045-2322
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Abstract
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.
| Item Type: | Article |
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| Keywords: | Cavity, direction-of-arrival estimation, lens, machine learning, and millimeter-wave (mmWave) |
| Divisions: | Applied Science, Computing and Engineering |
| Depositing User: | Hayley Dennis |
| Date Deposited: | 08 Jun 2022 12:02 |
| Last Modified: | 08 Jun 2022 12:02 |
| URI: | https://glyndwr.repository.guildhe.ac.uk/id/eprint/17889 |
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