Abstract
We generated dense mid-air volumetric acoustic holography using a newly developed computation algorithm and experimentally verified its utility for three-dimensional self-positioning via monaural amplitude measurements. We computed the holography by solving an inverse problem where complex amplitudes of ultrasound emissions are to be determined so that the given acoustic amplitude field at a finite set of control points in the workspace could be generated as faithfully as possible. With a one-directional gradient pattern of a 40 kHz ultrasonic field that stretched uniformly toward the depth direction, numerical simulations showed that positioning with an average error less than 3 mm is ideally possible in a 100 mm-sided cubic workspace. We experimentally verified that this error was approximately 8 mm with our custom-made phased array. Our work shows the first example of information systems, where a position-dependent information field is embedded in the environment as a form of holographic wave field that can be sensed by standalone mobile devices.
Highlights
Holography is a technique of generating desired wave fields out of phase-controlled emissions from multiple arrayed wave sources
With a one-directional gradient pattern of a 40 kHz ultrasonic field that stretched uniformly toward the depth direction, numerical simulations showed that positioning with an average error less than 3 mm is ideally possible in a 100 mm-sided cubic workspace
Our work shows the first example of information systems, where a position-dependent information field is embedded in the environment as a form of holographic wave field that can be sensed by standalone mobile devices
Summary
Holography is a technique of generating desired wave fields out of phase-controlled emissions from multiple arrayed wave sources. We propose volumetric acoustic holography based on the phased array technique, along with its application to the three-dimensional self-positioning of a microphone located inside a hologram. Spatial information of acoustic fields gives essential clues for source/microphone localization, which is typically captured by microphone arrays in the form of phase or amplitude differences between the received signal channels.[18,19]. These methods presuppose that a number of microphones are distributed in a spatial range, which is not suitable for miniature standalone devices. No vast prior learning is required, unlike for video-based methods
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