Distributed Sound Processing Node Using Convex Relaxed Beamforming
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Wireless sensor networks face challenges in maintaining speech intelligibility in noisy environments due to the decentralized nature of data collection, which hinders the calculation of beam-former outputs and estimation of covariance matrices, leading to inefficiencies and increased costs in centralized systems, and existing distributed algorithms are impractical for ad-hoc networks.
Innovation Solution
A distributed, statistically optimal beamforming approach using a convex relaxed version of the linearly constrained minimum variance method, allowing each sound processing node to determine weights for beamforming signals without a central point, through transformed versions of the linearly constrained minimum variance approach, including robust and dual domain transformations, and iterative algorithms like primal dual methods and min-sum message passing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a centralized fusion center is added to collect all data for processing, then beamforming output calculation and covariance matrix estimation are improved, but system reliability deteriorates due to single point of failure and additional costs for redundancy are required
Solution Approach 1:
The patent divides the centralized beamforming computation into distributed segments executed by individual sensor nodes. Each node independently computes its own beamforming weights using locally available data and information exchanged with neighboring nodes, eliminating the single point of failure while maintaining beamforming performance.
Solution Approach 2:
Each sensor node performs self-service by computing its own beamforming weights using local data and distributed communication. Nodes autonomously determine their weights through iterative exchange of covariance information with neighbors, removing dependence on a centralized fusion center and enhancing system reliability.
2Measurement precision
If a centralized fusion center is added to collect all data for processing, then beamforming output calculation is improved, but device complexity and cost increase due to over-specified memory and processing requirements
Solution Approach 1:
The computation task is segmented across multiple simple sensor nodes rather than concentrated in one complex fusion center. Each node handles only its local data and communicates with immediate neighbors, distributing the computational burden and reducing individual device complexity requirements.
Solution Approach 2:
Each sensor node operates with local quality by using only its locally available data and information from neighboring nodes to compute its beamforming weights. This eliminates the need for any node to have access to or process all global data, reducing memory and processing requirements at each location.
3Measurement precision
If a centralized fusion center is added to collect all data for processing, then beamforming output calculation is improved, but energy consumption increases due to excessive transmission costs depleting node battery life
Solution Approach 1:
The patent extracts the beamforming computation function from the centralized fusion center and distributes it to individual sensor nodes. This eliminates the need for continuous transmission of all raw data to a central point, as each node performs computations locally using distributed algorithms that exchange only necessary intermediate results.
Solution Approach 2:
The data transmission and processing is segmented into local exchanges between neighboring nodes rather than centralized collection. Each node communicates only with its immediate neighbors in the distributed network, significantly reducing the total transmission distance and energy consumption compared to all-to-central communication.
4Reliability
If distributed topologies are used to remove single point of failure, then system reliability is improved, but hardware requirements and memory use still scale with network size making deployment impractical
Solution Approach 1:
Each sensor node operates with local quality by computing beamforming weights using only local data and information from neighboring nodes. The memory requirements at each node scale only with the number of neighbors rather than the total network size, enabling practical deployment in large-scale distributed networks.
Solution Approach 2:
The global beamforming computation is segmented into local computations at each node. Each node handles its own data and exchanges information only with immediate neighbors, causing memory and computational requirements to scale locally rather than globally with network size.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
The invention relates to a sound processing node (101a) for an arrangement (100) of sound processing nodes (100a-c), the sound processing nodes (101a-c) being configured to receive a plurality of sound signals, wherein the sound processing node (101a) comprises a processor (103a) configured to determine a beam forming signal on the basis of the plurality of sound signals weighted by a plurality of weights, wherein the processor (103a) is configured to determine the plurality of weights using a transformed version of a linearly constrained minimum variance approach, the transformed version of the linearly constrained minimum variance approach being obtained by applying a convex relaxation to the linearly constrained minimum variance approach.