Spatiotemporal Beamforming for Mobile Acoustic Source Mapping
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Solution Overview
Problem
Current beamforming technologies face challenges in accurately monitoring acoustic energy in complex environments due to interference from background noise and the difficulty in isolating signals from multiple sources, especially when using static receiver arrays that cannot effectively capture broadband frequencies or distinguish between close sources.
Innovation Solution
A mobile, spatially-aware robotic platform with a sensor array that employs spatiotemporal beamforming to create high-resolution energy maps by moving across a space, isolating background noise associated with the platform itself, and synthesizing signals to enhance the detection of foreground signals of interest.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a static receiver array is used for beamforming, then the system structure is simple, but the system cannot effectively capture broadband frequencies or distinguish between close sources
Solution Approach 1:
The patent applies the dynamics principle by transitioning from a static receiver array to a mobile robotic platform that can move through the environment. The mobile platform carries sensors that dynamically reposition themselves to capture signals from multiple locations, enabling the system to distinguish between close sources and capture broadband frequencies that a static array cannot detect.
Solution Approach 2:
The patent implements another dimension by adding the temporal and spatial movement dimension to the traditional static beamforming approach. By moving the sensor array through three-dimensional space over time, the system creates a fourth dimension (time) and additional spatial dimensions for signal discrimination, allowing it to separate closely spaced sources that would be indistinguishable in a single static configuration.
2Measurement precision
If background noise is present in the sensor signal, then the measurement accuracy decreases, but filtering out the noise reduces the foreground signal quality
Solution Approach 1:
The patent applies the extraction principle by separating the background noise component from the foreground signal through spatial averaging. The system identifies and extracts the noise characteristics that are consistent across multiple locations, then removes this extracted noise component from the individual measurements, preserving the foreground signals that vary spatially.
Solution Approach 2:
The patent implements merging by combining measurements from multiple spatial locations through spatial averaging. By merging data from different positions in the environment, the system enhances the foreground signal while suppressing background noise, as the noise is statistically independent across locations while the foreground signal of interest remains consistent.
3Measurement precision
If multiple signal sources are present in the environment, then the energy map becomes complex to interpret, but isolating individual sources improves detection accuracy
Solution Approach 1:
The patent applies segmentation by dividing the complex multi-source environment into individual source components through spatially-resolved beamforming. The system segments the overlapping signals from multiple sources by exploiting their different spatial origins, creating separate energy maps for each source that can be independently analyzed.
Solution Approach 2:
The patent uses spatial averaging as an intermediary process between raw sensor data and final source identification. This intermediary step processes the complex multi-source signals by averaging across spatial locations, which simplifies the energy maps while preserving source information, making the data more interpretable without losing detection accuracy.
Data Source
AI summary
A method for imaging a room by a controller includes requesting a signal, indicative of a measurement of a parameter, from a sensor associated with a position and direction of a mobile platform in the room, wherein the measurement from the sensor includes background noise, distortions, and a foreground signal of interest, in response to the mobile platform reaching a new position, requesting a second measurement of the parameter from the sensor associated with the new position of the mobile platform, spatially aggregating the signal from the sensor and associated position and direction to create an energy map via spatio-dynamic beamforming, wherein the background noise and distortions are reduced, by spatially averaging beamformed information acquired across multiple locations to synthesize signals indicative of the foreground signal of interest, analyzing the energy map to identify a state of an apparatus in the room, and outputting a foreground beamformed image.