Wireless Environmental Sensing with Reduced Eigenvector Computation
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Solution Overview
Problem
Existing wireless environmental sensing technologies, such as AoA and distance sensing, require substantial processing resources, particularly for low-power devices, due to the need for computing a large number of eigenvectors of covariance matrices, which exceeds computational capabilities.
Innovation Solution
Implementing a method that samples and smooths covariance matrices to reduce computational complexity, using smoothed representations of sensing values, thereby reducing the number of processing operations required for accurate spatial characteristic estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a large number of eigenvectors of covariance matrices are computed for accurate spatial characteristic estimation, then measurement precision is improved, but device complexity and processing resource requirements worsen
Solution Approach 1:
The patent extracts only the essential information needed for spatial characteristic estimation by computing a reduced set of eigenvectors from the covariance matrix, rather than computing all eigenvectors. This extraction approach maintains measurement precision while significantly reducing computational complexity and processing resource requirements.
Solution Approach 2:
The patent segments the covariance matrix computation process into two stages: first computing the covariance matrix from sensing values, then extracting a reduced set of eigenvectors from this matrix. This segmentation allows the system to process spatial characteristics efficiently without requiring computation of all possible eigenvectors, thus reducing device complexity while maintaining accuracy.
2Device complexity
If the number of processing operations is reduced for computational efficiency, then device complexity is improved, but measurement precision may deteriorate
Solution Approach 1:
The patent changes the parameter of eigenvector computation from computing all eigenvectors to computing a reduced set of eigenvectors. This parameter change reduces the number of processing operations and computational complexity while maintaining sufficient measurement precision for accurate spatial characteristic estimation through optimized selection of essential eigenvectors.
3Measurement precision
If substantial processing resources are allocated to environmental sensing, then measurement precision is improved, but use of energy worsens
Solution Approach 1:
The patent extracts only the essential eigenvectors needed for accurate spatial characteristic estimation, avoiding computation of all possible eigenvectors. This extraction approach maintains sensing accuracy while significantly reducing energy consumption and processing resource requirements, making the system suitable for low-power wireless devices.
Data Source
AI summary
Implementations disclosed describe techniques and systems for efficient estimation of spatial characteristics of an outside environment of a wireless device. The disclosed techniques include generating multiple covariance matrices (CMs) representative of obtained sensing values. Different CMs may be associated with different frequency increments used in sensing signals to probe the outside environment. The disclosed techniques may further include determining eigenvectors for the CMs, and identifying, based on the determined eigenvectors, one or more spatial characteristics of the object in the outside environment.