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

Author:

Abbasi Muhammad Ali Babar,Akinsolu Mobayode O.,Liu Bo,Yurduseven Okan,Fusco Vincent F.,Imran Muhammad Ali

Abstract

AbstractThis 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.

Funder

Engineering and Physical Sciences Research Council

Leverhulme Trust

Publisher

Springer Science and Business Media LLC

Subject

Multidisciplinary

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