Method, apparatus, and system for wireless proximity detection

Patent metadata

Field	Value
Publication number	US-2024064691-A1
Application number	US-202318379622-A
Country	US
Kind code	A1
Filing date	Oct 12, 2023
Priority date	Feb 13, 2020
Publication date	Feb 22, 2024
Grant date	—

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 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.

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

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

4 . The method of claim 1 , further comprising:
computing a time-domain auto-correlation function (ACF) based on the CI in a first sliding time window of the TSCI, the time-domain ACF comprising a plurality of time-domain correlations; and computing the first motion statistics based on the time-domain ACF.

5 . The method of claim 4 , further comprising:
computing a feature of each CI in the TSCI, wherein the 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; and computing the time-domain ACF based on the feature of each CI in the first sliding time window of the TSCI.

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 first motion statistics 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 6 , further comprising:
computing multiple characteristic points of either the time-domain ACF or the function of the time-domain ACF; and computing the first motion statistics based on 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 first motion statistics based on 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 8 , further comprising:
computing a 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 probability score based on the STI; and computing the first motion statistics based on both the probability score and the difference of the first and second characteristic points.

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

13 . The method of claim 12 , wherein:
the probability density function comprises a mixture of pdf s, wherein at least one of the pdf's is a generalized Gaussian pdf.

14 . The method of claim 11 , wherein:
the first motion statistics is monotonic non-decreasing with respect to the probability score and with respect to the difference of the first and second characteristic points.

15 . The method of claim 1 , further comprising:
computing the frequency-domain correlation based on the CI in a second sliding time window of the TSCI.

16 . The method of claim 15 , further comprising:
computing a plurality of k-component correlations, wherein:
each CI comprises a representation with a plurality of components,
each of the plurality of components is associated with a respective frequency index,
each of the plurality of k-component correlations is a correlation between a respective component of a CI and a respective adjacent 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 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 arithmeti

Assignees

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

Inventors

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

Classifications

• H04W64/003 Primary

  locating network equipment · CPC title

• G01S11/06

  using intensity measurements · CPC title

• G01S13/003

  Bistatic radar systems; Multistatic radar systems · CPC title

• G01S7/006 Primary

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