Acoustic Vector Sensor Indoor Positioning
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Solution Overview
Problem
Existing location-based services struggle to accurately determine device position within indoor environments without requiring extensive hardware installation or complexity, as satellite-based systems are ineffective indoors and other technologies need base stations and additional hardware.
Innovation Solution
A method that processes acoustic vectors to determine spatial arrangement by capturing and processing vector components of ambient sound waves using acoustic vector sensors, converting them into electrical signals, and comparing with reference data to estimate device location, employing classifiers like convolutional neural networks for sound source classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If satellite-based positioning systems are used, then high accuracy location determination is achieved, but the system becomes ineffective in indoor environments
Solution Approach 1:
The patent replaces satellite-based electromagnetic positioning with an acoustic field-based positioning system that uses microphones and signal processing to determine device location indoors. The system captures acoustic signals from multiple microphones, processes them through signal processing circuits, and determines spatial arrangement based on acoustic vector data, enabling indoor localization without satellite dependency.
2Measurement precision
If RF or light-based wireless signaling technologies are used for indoor positioning, then location determination is achieved, but extensive hardware installation and device complexity are required
Solution Approach 1:
The patent enables the mobile device to perform self-positioning using its own acoustic sensors (microphones) and onboard signal processing capabilities. The device captures acoustic signals, processes them through integrated circuits, and determines its spatial arrangement autonomously without requiring external base stations, access points, or additional positioning hardware, thus reducing both installation and device complexity.
3Measurement precision
If acoustic vector sensors are used to capture vector components, then accurate spatial arrangement determination is achieved, but signal processing complexity increases
Solution Approach 1:
The patent divides the acoustic signal processing into distinct functional segments: acoustic signal capture by microphones, vector component extraction by signal processing circuits, and spatial arrangement determination by location services. This segmentation allows each component to handle specific processing tasks efficiently, managing complexity through modular functional decomposition.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables accurate device positioning within indoor environments without the need for extensive hardware, using acoustic vector sensors to capture and process sound data, effectively addressing the limitations of current technologies by providing a robust and hardware-efficient solution for location determination.
Implementation Method 1
an acoustic vector sensor arranged within the housing and in fluid communication with an environment surrounding the device via the acoustic port. The acoustic vector sensor is configured to capture a vector component of an acoustic field proximate to an exterior of the device at the acoustic port and convert the captured vector component into an electrical signal
Data Source
AI summary
Acoustic vector data is sensed via acoustic vector sensor configurations in mobile devices and used to generate sound fields. From these sound fields, positioning and orientation of the mobile device is derived. The sound fields and features derived from them are classified to provide mobile device position and other information about the environment from which the sound fields are captured. Additionally, sound fields are analyzed to detect gestural input and trigger associated programmatic actions.