Method, apparatus, and system for wireless monitoring with improved accuracy

Patent metadata

Field	Value
Publication number	US-12153156-B2
Application number	US-202318144321-A
Country	US
Kind code	B2
Filing date	May 8, 2023
Priority date	Jan 13, 2017
Publication date	Nov 26, 2024
Grant date	Nov 26, 2024

Abstract

Methods, apparatus and systems for wireless monitoring with improved accuracy are described. In one example, a described method comprises: transmitting a wireless signal through a wireless multipath channel of a venue, wherein the wireless multipath channel is impacted by a motion of an object in the venue; receiving 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 the motion; obtaining a time series of channel information (TSCI) of the wireless multipath channel based on the received wireless signal; performing a classification of a sliding time window by analyzing channel information (CI) of the TSCI in the sliding time window; computing a motion information (MI) for the sliding time window based on the TSCI and the classification of the sliding time window; and monitoring the motion of the object based on the MI.

First claim

We claim:

1. A method for wireless monitoring, comprising:
transmitting a wireless signal from a first wireless device through a wireless multipath channel of a venue, wherein the wireless multipath channel is impacted by a motion of an object in the venue;
receiving the wireless signal by a second wireless device through the wireless multipath channel, wherein the received wireless signal differs from the transmitted wireless signal due to the wireless multipath channel and the motion of the object;
obtaining a time series of channel information (TSCI) of the wireless multipath channel based on the received wireless signal using a processor, a memory communicatively coupled with the processor and a set of instructions stored in the memory;
performing a classification of a sliding time window by analyzing channel information (CI) of the TSCI in the sliding time window;
computing a motion information (MI) for the sliding time window based on the TSCI and the classification of the sliding time window, wherein:
the MI is computed in a first manner based exclusively on CI of the TSCI in the sliding time window when the sliding time window is classified as a first sliding-window-class based on the classification,
the MI is computed in a second manner based on at least one CI of the TSCI outside the sliding time window when the sliding time window is classified as a second sliding-window-class based on the classification,
the MI is computed in a third manner based on a first subset of the CI of the TSCI in the sliding time window without using a second subset of the CI of the TSCI in the sliding time window, when the sliding time window is classified as a third sliding-window-class based on the classification, wherein the first subset and the second subset are disjoint; and

monitoring the motion of the object based on the MI.

2. The method of claim 1 , wherein the MI is computed based on at least one of:
a similarity score of two temporally adjacent CI of the TSCI;
an autocorrelation function (ACF) of the TSCI; or
a characteristic point of the ACF.

3. The method of claim 1 , further comprising: computing a test score (TS) for each CI of the TSCI in the sliding time window based on a number of respective temporally neighboring CI, wherein the TS comprises at least one of: a difference, a magnitude, a vector similarity, a vector dissimilarity, an inner product, or an outer product; and classifying each CI of the TSCI in the sliding time window based on the respective TS.

4. The method of claim 3 , further comprising:
computing a link-wise test score (LTS) based on an aggregate of all TS for the CI of the TSCI in the sliding time window; and
performing the classification of the sliding time window based on the LTS.

5. The method of claim 4 , wherein:
the sliding time window is classified as the first sliding-window-class when the LTS is greater than a first threshold; and
the sliding time window is classified as the second sliding-window-class or the third sliding window-class when the LTS is less than a second threshold.

6. The method of claim 5 , wherein:
each CI of the TSCI in the sliding time window is classified as a first CI-class when the respective TS is less than a third threshold, and classified as a second CI-class when the respective TS is greater than a fourth threshold.

7. The method of claim 6 , wherein:
the sliding time window is classified as the first sliding-window-class when all CI of the TSCI in the sliding time window are first-class CI classified as first CI-class; and
the sliding time window is classified as the second sliding-window-class when all CI of the TSCI in the sliding time window are second-class CI classified as second CI-class, wherein the TSCI in the sliding time window comprises at least one first-class CI and at least one second-class CI.

8. The method of claim 7 , further comprising:
identifying at least one run of first-class CI and at least one run of second-class CI in the sliding time window, each run comprising a respective runlength of consecutive CI of a respective same CI-class in the sliding time window, wherein any runlength is one of: a number, a quantity or a count, that is greater than zero; and
classifying the sliding time window based on the runs of first-class CI and second-class CI and the respective run-lengths.

9. The method of claim 8 , further comprising:
the sliding time window is classified as the third sliding-window-class when at least one selected run of first-class CI is chosen; and
the sliding time window is classified as the second sliding-window-class when no selected run of first-class CI is chosen.

10. The method of claim 9 , further comprising:
choosing at least one selected run of first-class CI based on the runlength of each run of first-class CI and the TS associated with the run; and
computing the MI based on the at least one selected run of first-class CI, wherein the at least one selected run is the at least one run of first-class CI with longest run-lengths among all runs of first-class CI in the sliding time window.

11. The method of claim 10 , wherein:
a first selected run is chosen as a beginning run of consecutive first-class CI comprising a CI being the first in the sliding time window when the respective run-length of the beginning run is greater than a first respective threshold;
a second selected run is chosen as a trailing run of consecutive first-class CI comprising a CI being the last in the sliding time window when the respective run-length of the trailing run is greater than a second respective threshold; and
any selected run that is not a beginning run or a trailing run is chosen when the respective run-length of the run is greater than a third respective threshold.

12. The method of claim 11 , wherein:
the at least one selected run is chosen such that a quantity of the at least one selected run is less than or equal to a predetermined number; and
the at least one selected run is chosen such that, for each selected run, all the associated TS is less than a threshold.

13. The method of claim 12 , further comprising:
when the sliding time window is classified as the third sliding-window-class:
constructing the first subset by including all of the at least one selected run of first-class CI of the TSCI in the sliding time window, and
constructing the second subset by including all second-class CI of the TSCI in the sliding time window; and

when the sliding time window is classified as the second sliding-window-class:
computing the MI as an aggregate of at least one neighboring MI,
wherein each neighboring MI is associated with one of: a past neighboring sliding time window of CI of the TSCI, a future neighboring sliding time window of CI of the TSCI, or a neighboring sliding time window of CI of another TSCI,
wherein a neighboring MI is computed based on at least one CI of the TSCI outside the sliding time window.

14. The method of claim 13 , further comprising:
computing at least one tentative MI for the sliding time window, wherein each tentative MI is computed based on a respective selected run of first-class CI of the TSCI in the sliding time window; and
computing the MI as an aggregate of the at least one tentative MI.

15. The method of claim 14 , further comprising:
determining a selected run as a beginning run of CI of the TSCI in the sliding time window;
combining the beginning run of CI with a trailing run of CI of the TSCI in a previous sliding time window to form a combined run of CI of the TSCI; and
computing a first tentative MI based on the combined run o

Assignees

• Ozturk Muhammed Zahid
• Regani Sai Deepika
• Wang Beibei
• Liu K J Ray
• Lai Hung Quoc Duc
• Han Yi
• Au Oscar Chi Lim
• Origin Res Wireless Inc

Inventors

• Ozturk Muhammed Zahid
• Regani Sai Deepika
• Wang Beibei
• Liu K J Ray
• Lai Hung-Quoc Duc
• Han Yi
• Au Oscar Chi-Lim

Classifications

• G01S13/003

  Bistatic radar systems; Multistatic radar systems · CPC title

• H04W24/08 Primary

  Testing, {supervising or monitoring} using real traffic · 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

• G01S7/415

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