Wireless Sensing Circuit Using Eigenvalue Decomposition for Instantaneous Vibration Detection
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Solution Overview
Problem
Existing wireless sensing technologies face challenges in estimating range, vibration frequency, and incident angle due to dependencies on sampling frequency, observation time, and the number of receiving antennas, which can increase hardware costs and limit the ability to detect instantaneous changes.
Innovation Solution
A circuit and method that generate a phase vector, estimate a phase matrix, decompose it into eigenvalue and eigenvector matrices, perform long-term averaging, compute difference values, and generate a pseudo spectrum to determine vibration frequencies and incident angles, independent of sampling frequency, observation time, and the number of antennas.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the number of receiving antennas is increased to improve measurement precision, then the sensing accuracy is improved, but the hardware cost increases
Solution Approach 1:
The patent changes the processing parameters (performing long-term average on eigenvalues, computing difference values, generating pseudo spectrum) instead of changing hardware parameters (number of antennas). This allows achieving high sensing accuracy through advanced signal processing algorithms rather than increasing the number of receiving antennas, thus resolving the contradiction between measurement precision and hardware cost.
2Measurement precision
If the observation time is increased to improve measurement precision, then the FFT resolution is improved, but the ability to detect instantaneous changes is reduced
Solution Approach 1:
The patent implements a dynamic processing approach by performing long-term average on eigenvalues and computing difference values to generate pseudo spectrum. This dynamic processing method enables the system to adapt to instantaneous changes while maintaining high resolution, overcoming the limitation of fixed observation time in traditional FFT-based methods.
Solution Approach 2:
The patent transforms the static FFT resolution limitation into a dynamic processing advantage by changing how eigenvalues are processed (long-term average) and how the spectrum is generated (pseudo spectrum from difference values). This allows the system to achieve high resolution without being constrained by fixed observation time, enabling detection of instantaneous changes.
3Measurement precision
If the sampling frequency is increased to improve measurement precision, then the sensing accuracy is improved, but the processing complexity increases
Solution Approach 1:
The patent replaces traditional mechanical signal processing methods (direct FFT processing requiring high sampling frequency) with an alternative approach based on eigenvalue decomposition and pseudo-spectrum generation. This substitution allows achieving high sensing accuracy without relying on high sampling frequency, thereby reducing processing complexity while maintaining measurement precision.
Data Source
AI summary
A processing circuit includes: an estimating circuit, for generating a phase vector according to a phase signal, and for estimating the phase vector to generate an estimated phase matrix; a decomposing circuit, for decomposing the estimated phase matrix to generate an eigenvalue matrix and an eigenvector matrix; a first computing circuit, for performing a long-term average for a plurality of eigenvalues to generate a plurality of long-term eigenvalues; a second computing circuit, for computing a plurality of difference values for the plurality of long-term eigenvalues, and for determining an index corresponding to a difference value of the plurality of difference values; a spectrum generation circuit, for generating a pseudo spectrum according to the index, a plurality of eigenvectors and a steering vector; and a determining circuit, for determining at least one peak of the pseudo spectrum and at least one parameter corresponding to the at least one peak.