Machine learning-assisted lens-loaded cavity response optimization for improved direction-of-arrival estimation

Abbasi, M. A. B., Akinsolu, M. O., Liu, B. , 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. (doi: 10.1038/s41598-022-12011-z)

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: Articles
• Additional Information: This work was partially funded by the Engineering and Physical Sciences Research Council under grant EP/P000673/1 and by Leverhulme Trust under Research Leadership Award RL-2019-019.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Imran, Professor Muhammad and Liu, Professor Bo
• Authors: Abbasi, M. A. B., Akinsolu, M. O., Liu, B., Yurduseven, O., Fusco, V. F., and Imran, M. A.
• College/School: College of Science and Engineering > School of Engineering > Autonomous Systems and Connectivity
• Journal Name: Scientific Reports
• Publisher: Nature Research
• ISSN: 2045-2322
• ISSN (Online): 2045-2322
• Copyright Holders: Copyright © 2022 The Authors
• First Published: First published in Scientific Reports 12: 8511
• Publisher Policy: Reproduced under a Creative Commons License