Method, apparatus, and system for wireless proximity detection

Patent metadata

Field	Value
Publication number	US-12256360-B2
Application number	US-202318379622-A
Country	US
Kind code	B2
Filing date	Oct 12, 2023
Priority date	Feb 13, 2020
Publication date	Mar 18, 2025
Grant date	Mar 18, 2025

Abstract

Methods, apparatus and systems for wireless sensing and proximity detection are described. For example, a described method comprises: transmitting, by a first wireless device, a wireless signal through a wireless multipath channel in a venue; receiving, by a second wireless device, the wireless signal through the wireless multipath channel, wherein the received wireless signal differs from the transmitted wireless signal due to the wireless multipath channel and a movement of an object in the venue; obtaining a time series of channel information (TSCI) of the wireless multipath channel based on the received wireless signal; computing a first motion statistics based on a time-domain correlation of channel information (CI) in the TSCI; computing a second motion statistics based on a frequency-domain correlation of the CI in the TSCI; and detecting a proximity of the object to a target in the venue based on the first motion statistics and the second motion statistics.

First claim

We claim:

1. A method for wireless sensing, comprising:
transmitting, by a first wireless device, a wireless signal through a wireless multipath channel in a venue;
receiving, by a second wireless device, the wireless signal through the wireless multipath channel, wherein the received wireless signal differs from the transmitted wireless signal due to the wireless multipath channel and a movement of an object in the venue;
obtaining a time series of channel information (TSCI) of the wireless multipath channel based on the received wireless signal;
computing a spatial-temporal information (STI) based on a time-domain auto-correlation function (ACF) of channel information (CI) in a first sliding time window of the TSCI computed based on a first feature of each CI;
computing a gait score based on the STI;
computing a time series of resampled CI (TSRCI) by resampling the TSCI;
computing a proximity score based on a frequency-domain k-component correlation of resampled CI (RCI) in a second sliding time window of the TSRCI computed based on a second feature of each RCI; and
detecting a proximity state of the object to a target in the venue based on the gait score and the proximity score in a third sliding time window.

2. The method of claim 1 , further comprising:
computing, based on a first time trend of the gait score and a second time trend of the proximity score, a third time trend of the proximity state of the object to the target.

3. The method of claim 1 , further comprising:
computing the gait score repeatedly at a first repetition rate, each gait score being computed based on a respective first sliding window of the TSCI;
computing the proximity score repeatedly at a second repetition rate, each proximity score being computed based on a respective second sliding window of the TSCI; and
computing the proximity state of the object to the target repeatedly at a third repetition rate.

4. The method of claim 1 , wherein:
the time-domain ACF comprises a plurality of time-domain correlations.

5. The method of claim 4 ,
wherein the first feature comprises one of: a magnitude, a phase, a magnitude of a component of the CI, a phase of a component of the CI, a magnitude square, or a function of the magnitude.

6. The method of claim 4 , further comprising:
computing at least one characteristic point of either the time-domain ACF or a function of the time-domain ACF; and
computing the STI based on the at least one characteristic point,
wherein the at least one characteristic point comprises at least one of:
a global maximum, a global minimum, a constrained maximum, a constrained minimum, a maximum restricted to a positive argument of the ACF or the function of the ACF, a maximum restricted to a negative argument of the ACF or the function of the ACF, a minimum restricted to a positive argument of the ACF or the function of the ACF, a minimum restricted to a negative argument of the ACF or the function of the ACF, a local maximum, a local minimum, a first local maximum, a first local minimum, a second local maximum, a second local minimum, a third local maximum, a third local minimum, an inflection point, a zero-crossing point, a mean-crossing point, a first inflection point, a first zero-crossing point, a first mean-crossing point, a second inflection point, a second zero-crossing point, a second mean-crossing point, a third inflection point, a third zero-crossing point, or a third mean-crossing point;

wherein the function of the time-domain ACF comprises at least one of:
a linear function, a piecewise linear function, a nonlinear function, a polynomial function, an exponential function, a logarithmic function, a trigonometric function, a transcendental function, a derivative function, a first derivative, a second derivative, a third derivative, an integration function, a single integration, a double integration, a triple integration, an absolute function, a magnitude function, an indicator function, a thresholding function, a quantization function, or
a function obtained by filtering of the time-domain ACF, the filtering comprising at least one of: a lowpass filtering, a bandpass filtering, a highpass filtering, a smoothing filtering, or a weighted averaging.

7. The method of claim 1 , further comprising:
computing multiple characteristic points of either the time-domain ACF or the function of the time-domain ACF; and
computing the gait score based on the STI and an additional function of the multiple characteristic points.

8. The method of claim 7 , further comprising:
computing a local maximum as a first characteristic point of either the time-domain ACF or the function of the time-domain ACF;
computing a local minimum as a second characteristic point of either the time-domain ACF or the function of the time-domain ACF;
computing a difference of the first and second characteristic points by subtracting the local minimum from the local maximum; and
computing the gait score based on the STI and the difference.

9. The method of claim 8 , wherein:
the local maximum is the first local maximum with argument being positive; and
the local minimum is the first local minimum with argument being positive.

10. The method of claim 8 , wherein:
when at least one of the first characteristic point or the second characteristic point cannot be determined, the difference of the first and second characteristic points is computed as zero.

11. The method of claim 6 , further comprising:
computing the spatial-temporal information (STI) based on a third characteristic point of either the time-domain ACF or the function of the time-domain ACF, wherein the STI comprises one of: a location, a distance, a speed, or an acceleration;
computing a STI probability score based on the STI; and
computing the gait score based on the STI probability score.

12. The method of claim 11 , further comprising:
computing the STI probability score based on a STI probability density function (pdf).

13. The method of claim 12 , wherein:
the STI pdf comprises a mixture of pdf's, wherein at least one of the pdf's is a generalized Gaussian pdf obtained in a training phase.

14. The method of claim 11 , wherein:
the gait score is a monotonic non-decreasing function of the STI probability score and the difference of the first and second characteristic points.

15. The method of claim 1 , wherein the second feature comprises one of: a magnitude, a phase, a magnitude of a component of the RCI, a phase of a component of the RCI, a magnitude square, or a function of the magnitude.

16. The method of claim 1 , further comprising:
computing a plurality of frequency-domain k-component correlations, wherein:
each CI comprises a representation with a plurality of frequency-domain components,
each of the plurality of frequency-domain components is associated with a respective frequency index,
each of the plurality of k-component correlations is a correlation between a respective frequency-domain component of a CI and a respective adjacent frequency-domain component of the CI,
the frequency indices of the respective component and the respective adjacent component of the CI differ by k,
k is an integer greater than zero; and

computing the frequency-domain k-component correlation based on an aggregate of the plurality of k-component correlations, wherein the aggregate comprises at least one of: a sum, a weighted sum, a mean, an average, a weighted mean, a trimmed mean, a product, a weighted product, an arithmetic mean, a geometric mean, a harmonic mean, or another aggregate of any of the above.

Assignees

• Hu Yuqian
• Zhu Guozhen
• Wang Beibei
• Liu K J Ray
• Au Oscar Chi Lim
• Origin Res Wireless Inc

Inventors

• Hu Yuqian
• Zhu Guozhen
• Wang Beibei
• Liu K J Ray
• Au Oscar Chi-Lim

Classifications

• G01S11/06

  using intensity measurements · CPC title

• G01S13/003

  Bistatic radar systems; Multistatic radar systems · CPC title

• H04W64/003 Primary

  locating network equipment · CPC title

• G01S7/006 Primary

  using shared front-end circuitry, e.g. antennas (G01S13/765, G01S13/825 take precedence) · CPC title