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

• Published in: Scientific reports Vol. 12; no. 1; pp. 8511 - 13
• Main Authors: Abbasi, Muhammad Ali Babar, Akinsolu, Mobayode O., Liu, Bo, Yurduseven, Okan, Fusco, Vincent F., Imran, Muhammad Ali
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 20.05.2022; Nature Publishing Group; Nature Portfolio
• Subjects: 639/166/987; 639/766/25; 639/766/930; Humanities and Social Sciences; Learning algorithms; Machine learning; multidisciplinary; Optimization techniques; Radiation; Science; Science (multidisciplinary); Wave direction
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-022-12011-z

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.