Method, apparatus, and system for wireless proximity sensing

Patent metadata

Field	Value
Publication number	US-11500058-B2
Application number	US-202117214841-A
Country	US
Kind code	B2
Filing date	Mar 27, 2021
Priority date	Jul 17, 2015
Publication date	Nov 15, 2022
Grant date	Nov 15, 2022

Abstract

Methods, apparatus and systems for wireless proximity sensing are described. In one example, a described system comprises: a transmitter configured for transmitting a first wireless signal through a wireless multipath channel of a venue; a receiver configured for receiving a second wireless signal through the wireless multipath channel; and a processor. The second wireless signal differs from the first wireless signal due to the wireless multipath channel that is impacted by a movement of an object in the venue. The processor is configured for: obtaining a time series of channel information (TSCI) of the wireless multipath channel based on the second wireless signal, wherein each channel information (CI) of the TSCI comprises a plurality of CI components, each of which is associated with an index; computing an inter-component statistics based on the plurality of CI components; computing, based on the inter-component statistics, a proximity information of the object with respect to a reference location in the venue; and performing a task based on the proximity information of the object.

First claim

We claim:

1. A system for wireless proximity sensing, comprising:
a transmitter configured for transmitting a first wireless signal through a wireless multipath channel of a venue;
a receiver configured for receiving a second wireless signal through the wireless multipath channel, wherein the second wireless signal differs from the first wireless signal due to the wireless multipath channel that is impacted by a movement of an object in the venue; and
a processor configured for:
obtaining a time series of channel information (TSCI) of the wireless multipath channel based on the second wireless signal, wherein each channel information (CI) of the TSCI comprises a plurality of CI components, each of which is associated with an index,
selecting multiple pairs of CI components from the plurality of CI components of each CI obtained from the second wireless signal, based on a common number that is less than fifty, wherein each selected pair of CI components comprises a first CI component associated with a first index and a second CI component associated with a second index, wherein a difference between the first index and the second index is equal to the common number,
computing a plurality of multi-component statistics, each of which is computed based on a number of CI components among the plurality of CI components, wherein:
all of the plurality of multi-component statistics are computed based on a same quantity of CI components,
each of the plurality of multi-component statistics is a pairwise statistics computed based on a pair of CI components among the plurality of CI components,
each of the plurality of pairwise statistics is associated with a respective selected pair of CI components among the multiple selected pairs of CI components,

computing an inter-component statistics based on the plurality of pairwise statistics,
computing, based on the inter-component statistics, a proximity information of the object with respect to a reference location in the venue, and
performing a task based on the proximity information of the object.

2. The system of claim 1 , wherein:
the reference location is a location of the transmitter or a location of the receiver.

3. The system of claim 1 , wherein:
a neighborhood of the reference location is segmented to a number of segments; and
the proximity information associates the object with a particular segment of the number of segments.

4. The system of claim 3 , wherein:
the object is associated with the particular segment based on the inter-component statistics.

5. The system of claim 1 , wherein:
the proximity information of the object includes a proximity classification of the object with respect to a set of proximity classes with respect to the reference location.

6. The system of claim 5 , wherein the processor is further configured for:
classifying the object with respect to the set of proximity classes based on the inter-component statistics.

7. The system of claim 1 , wherein computing the inter-component statistics comprises:
computing a plurality of CI component features, each of which is a feature of a respective CI component of the plurality of CI components, wherein each of the plurality of CI component features comprises a function of at least one of: magnitude, phase, real component, imaginary component, or mapping, of the respective CI component; and
computing the inter-component statistics based on the plurality of CI component features.

8. The system of claim 7 , wherein:
the inter-component statistics is computed at a current time based on a time window associated with the current time.

9. The system of claim 8 , wherein:
computing the plurality of CI component features comprises computing CI component features of respective CI components of each CI of the TSCI in the time window associated with the current time; and
the inter-component statistics is computed at the current time based on the CI component features of the respective CI components of each CI of the TSCI in the time window.

10. The system of claim 9 , wherein computing the inter-component statistics further comprises:
computing a plurality of multi-component statistics, each of which is computed based on the CI component features of the respective CI components of each CI of the TSCI in the time window, wherein the inter-component statistics is computed at the current time based on the plurality of multi-component statistics.

11. The system of claim 9 , wherein computing the inter-component statistics further comprises:
computing a plurality of pairwise statistics, each of which is computed based on CI component features of a pair of CI components of each CI of the TSCI in the time window, wherein the inter-component statistics is computed at the current time based on the plurality of pairwise statistics.

12. The system of claim 11 , wherein computing the plurality of pairwise statistics comprises:
for each respective CI of the TSCI in the time window, computing a respective number of pairwise statistics, each of which is computed based on CI component features of a respective selected pair of CI components of the respective CI.

13. The system of claim 12 , wherein computing the inter-component statistics further comprises:
for each respective CI of the TSCI in the time window, computing a first representative value based on the respective number of pairwise statistics; and
computing a second representative value as the inter-component statistics at the current time, based on the first representative values associated with all CI of the TSCI in the time window,
wherein each of the first representative value and the second representative value is computed based on a respective one of: a sum, weighted sum, average, weighted average,
arithmetic mean, geometric mean, harmonic mean, trimmed mean, median, mode, or percentile.

14. The system of claim 12 , wherein computing the inter-component statistics further comprises:
for each selected pair of CI components of each CI of the TSCI in the time window, computing a first representative value based on a respective number of pairwise statistics, each of which is computed based on CI component features of the selected pair of CI components; and
computing a second representative value as the inter-component statistics at the current time, based on the first representative values associated with all selected pairs of CI components of all CI of the TSCI in the time window,
wherein each of the first representative value and the second representative value is computed based on a respective one of: a sum, weighted sum, average, weighted average, arithmetic mean, geometric mean, harmonic mean, trimmed mean, median, mode, or percentile.

15. The system of claim 14 , wherein the inter-component statistics comprise at least one of the following two-component statistics:
product, quotient, ratio, sum, difference, correlation, or cross covariance.

16. The system of claim 1 , wherein the proximity information of the object is computed based on an application of at least one of the following to the inter-component statistics:
feature extraction, projection, decomposition, mapping, feature training, transform, machine learning, maximum likelihood, labeling, optimization, or normalization.

17. The system of claim 1 , wherein performing the task comprises at least one of:
controlling or turning on or off one of: a camera, microphone, dialogue system, door bell, illumination, or security device;
opening or closing one of: a door, window, entrance, exit, or lighting; or
controlli

Assignees

• Hu Yuqian
• Wang Beibei
• Ozturk Muhammed Zahid
• Zhang Feng
• Liu K J Ray
• Au Oscar Chi Lim
• Zeng Xiaolu
• Wu Chenshu
• Wang Fengyu
• Origin Wireless Inc

Inventors

• Hu Yuqian
• Wang Beibei
• Ozturk Muhammed Zahid
• Zhang Feng
• Liu K J Ray
• Au Oscar Chi-Lim
• Zeng Xiaolu
• Wu Chenshu
• Wang Fengyu

Classifications

• G01S13/003

  Bistatic radar systems; Multistatic radar systems · CPC title

• G01S7/415 Primary

  Identification of targets based on measurements of movement associated with the target · CPC title

• G01S7/006 Primary

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

• G01S13/56

  for presence detection {(presence detection using near field arrangements G01V3/00, e.g. G01V3/08, G01V3/12; burglar, theft or intruder alarms with electrical actuation G08B13/22 - G08B13/26)} · CPC title

• H04W84/18

  Self-organising networks, e.g. ad-hoc networks or sensor networks · CPC title