Spatiotemporal Beamforming for Mobile Platform Self-Noise Isolation
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Solution Overview
Problem
Existing beamforming technologies struggle to accurately separate foreground signals from background noise, especially in dynamic environments where the recording platform moves, leading to interference from self-noise and challenges in identifying the direction of arrival and frequency of acoustic signals.
Innovation Solution
A system and method using spatiotemporal beamforming with a mobile platform, employing singular value decomposition to separate background noise from foreground signals, enabling self-diagnosis and self-calibration to improve signal quality and health monitoring of the recording platform.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If beamforming is used to map energy distribution of signal sources, then directional signal enhancement is improved, but background noise interference worsens
Solution Approach 1:
The patent segments the signal space into foreground (desired signal) and background (noise) subspaces using singular value decomposition. This mathematical segmentation separates the signal of interest from interfering background noise, allowing independent processing and enhancement of directional signals while suppressing noise components.
Solution Approach 2:
The patent extracts and removes background noise from the beamformed signal by identifying and eliminating the background subspace components. This extraction process isolates the foreground signal containing directional information while discarding the noise-containing background components, thereby improving signal quality.
2Area of stationary object
If mobile platform is used for data acquisition, then spatial coverage is improved, but self-noise interference worsens
Solution Approach 1:
The patent performs preliminary identification and characterization of the mobile platform's self-noise profile before main signal acquisition. By pre-characterizing the noise sources generated by the mobile platform's movement and operation, the system can apply targeted noise removal algorithms during signal processing, effectively isolating desired signals from self-generated interference.
Solution Approach 2:
The patent implements a feedback mechanism where the processed signal quality is continuously monitored and used to adjust noise removal parameters. The system feeds back the identified noise characteristics and signal quality metrics to refine the beamforming and noise subtraction processes, adaptively optimizing performance as the mobile platform moves through different environments.
3Measurement precision
If subspace approximation using singular value decomposition is applied, then signal separation is improved, but computational complexity worsens
Solution Approach 1:
The patent applies partial singular value decomposition by computing only the dominant singular values and vectors needed for noise subspace identification, rather than performing complete SVD on all signal components. This partial computation approach achieves sufficient signal-separation accuracy while significantly reducing computational burden and processing time.
Data Source
AI summary
A method for self-diagnosing a data acquisition system for acquiring calibrated images of an area by a controller includes requesting a signal, from a sensor associated with a mobile platform in the area, removing from the signal, background noise associated with the mobile platform, thereby focusing the measurement to a foreground signal, wherein the background noise is removed from the foreground signal via a subspace approximation using singular value, requesting a previous-in-time signal indicative of a previous-in-time measurement of the parameter, wherein previous-in-time background noise is removed from a previous-in-time foreground signal via subspace approximation using singular value decomposition and in response to a change detection indicating a difference between a spectrogram of the background noise and a previous-in-time spectrogram of the previous-in-time background noise exceeding a predetermined threshold at a predetermined frequency, outputting a status signal indicative of a change in operating characteristics of the mobile platform.