Method, apparatus, and system for correlation-based wireless monitoring and localization

Patent metadata

Field	Value
Publication number	US-12044790-B2
Application number	US-202318211567-A
Country	US
Kind code	B2
Filing date	Jun 19, 2023
Priority date	Feb 13, 2020
Publication date	Jul 23, 2024
Grant date	Jul 23, 2024

Abstract

Methods, apparatus and systems for correlation-based wireless monitoring are described. For example, a described method comprises: detecting and monitoring motion of a first object in a first sensing task based on a first motion information (MI) computed based on a first time series of channel information (TSCI) associated with a first device pair; detecting and monitoring motion of a second object in a second sensing task based on a second MI computed based on a second TSCI associated with a second device pair; computing a correlation score based at least partially on: the first TSCI, the second TSCI, the first MI and the second MI; detecting the first object and the second object as a same object when the correlation score is greater than a first threshold; and detecting the first object and the second object as two different objects when the correlation score is less than a second threshold.

First claim

We claim:

1. A method for correlation-based wireless monitoring, comprising:
forming, by a plurality of first wireless devices and a plurality of second wireless devices in a venue, at least two device pairs, each device pair comprising a first wireless device and a second wireless device, wherein the venue includes a number of objects each undergoing a respective motion;
for each device pair:
transmitting, by the first wireless device of the device pair, a respective wireless signal,
receiving, by the second wireless device of the device pair, the respective wireless signal through a respective wireless multipath channel of the venue, wherein the received wireless signal differs from the transmitted wireless signal due to the respective wireless multipath channel and the motions of the number of objects in the venue,
obtaining a respective time series of channel information (TSCI) of the respective wireless multipath channel based on the received wireless signal,
computing a respective motion information (MI) based on the TSCI, and
performing a respective sensing task based on the respective MI and the respective TSCI;

detecting and monitoring motion of a first object in a first sensing task based on a first MI computed based on a first TSCI associated with a first device pair;
detecting and monitoring motion of a second object in a second sensing task based on a second MI computed based on a second TSCI associated with a second device pair;
computing a correlation score based at least partially on: the first TSCI, the second TSCI, the first MI and the second MI;
detecting the first object and the second object as a same object when the correlation score is greater than a first threshold;
detecting the first object and the second object as two different objects when the correlation score is less than a second threshold;
locating the first object and the second object by associating the first object with a first zone in the venue and associating the second object with a second zone in the venue, when the correlation score is less than the second threshold, wherein
the first zone is a neighborhood around: a location of the first wireless device of the first device pair, a location of the second wireless device of the first device pair, or both,
the second zone is a neighborhood around: a location of the first wireless device of the second device pair, a location of the second wireless device of the second device pair, or both; and

locating the first object and the second object by associating the same object representing the first object and the second object with a first derived zone related to the first zone and the second zone in the venue, when the correlation score is greater than the first threshold, wherein the first derived zone comprises at least one of: an intersection of the first zone and the second zone, or a union of the first zone and the second zone.

2. The method of claim 1 , further comprising:
aligning the first MI and the second MI in the time domain; and
computing the correlation score between the aligned first MI and the aligned second MI.

3. The method of claim 1 , further comprising:
computing a time series of first MI based on the first TSCI, wherein each first MI is computed based on a respective first sliding window of the first TSCI;
computing a time series of second MI based on the second TSCI, wherein each second MI is computed based on a respective second sliding window of the second TSCI; and
computing the correlation score between the time series of first MI and the time series of second MI.

4. The method of claim 1 , further comprising:
computing a time series of first MI based on the first TSCI, wherein each first MI is computed based on a respective first sliding window of the first TSCI;
computing a time series of second MI based on the second TSCI, wherein each second MI is computed based on a respective second sliding window of the second TSCI; and
computing the correlation score between a time window of the time series of first MI and the same time window of the time series of second MI.

5. The method of claim 4 , wherein:
determining that the time window covers N1 first MI in the time series of first MI and N2 second MI in the time series of second MI;
resampling the time series of first MI to generate N3 aligned first MI in the time window;
resampling the time series of second MI to generate N3 aligned second MI in the time window, wherein each of the N3 aligned second MI is time aligned with a respective one of the N3 aligned first MI; and
computing the correlation score based on the N3 aligned first MI and the N3 aligned second MI in the time window.

6. The method of claim 1 , wherein a MI computed based on a TSCI comprises at least one of:
an average within a sliding time window;
a magnitude of each CI in the sliding time window;
a magnitude square of each component of each CI in the sliding time window;
a correlation between two temporally adjacent CI of the TSCI;
a similarity score between two temporally adjacent CI of the TSCI;
an inner product of two vectors of temporally adjacent CI of the TSCI;
an average of component-wise correlation between components of two temporally adjacent CI of the TSCI; or
a weighted average of a number of largest component-wise correlation.

7. The method of claim 1 , wherein the correlation score is computed based on at least one of: correlation, correlation coefficient, absolute value of correlation, or a monotonic function of the absolute value of correlation, between the first MI and the second MI.

8. The method of claim 1 , wherein the first sensing task and the second sensing task are different.

9. The method of claim 1 , wherein the first MI and the second MI are the same mathematically or statistically.

10. The method of claim 9 , wherein:
the first sensing task and the second sensing task comprise a common subtask; and
both the first MI and the second MI are used to perform the common subtask.

11. The method of claim 10 , wherein:
the common subtask comprises motion detection.

12. The method of claim 1 , further comprising:
determining that the correlation score is greater than the first threshold;
detecting the first object and the second object as a first common object because the correlation score is greater than the first threshold;
computing a first representative MI associated with the first common object representing the first object and the second object based on the first MI and the second MI;
detecting and monitoring motion of a third object in a third sensing task based on a third MI computed based on a third TSCI associated with a third device pair;
computing a second correlation score between the third MI and the first representative MI based on: the first TSCI, the second TSCI, the third TSCI, the first representative MI and the third MI;
detecting the third object and the first common object as a same object when the second correlation score is greater than the first threshold; and
detecting the third object and the first common object as two different objects when the second correlation score is less than the second threshold.

13. The method of claim 12 , further comprising:
determining that the second correlation score is greater than the first threshold;
detecting the first object, the second object and the third object as the first common object because the second correlation score is greater than the first threshold;
computing a second representative MI associated with the first common object representing the first object, the second object and the third object based on the firs

Assignees

• Regani Sai Deepika
• Hu Yuqian
• Wang Beibei
• Liu K J Ray
• Au Oscar Chi Lim
• Origin Res Wireless Inc

Inventors

• Regani Sai Deepika
• Hu Yuqian
• Wang Beibei
• Liu K J Ray
• Au Oscar Chi-Lim

Classifications

• G01S5/0278

  involving statistical or probabilistic considerations (G01S5/0252, G01S5/0294 take precedence) · CPC title

• G01S2205/02

  Indoor · CPC title

• H04W64/006

  with additional information processing, e.g. for direction or speed determination · CPC title

• G01S5/0273 Primary

  using multipath or indirect path propagation signals in position determination · CPC title

• G01S2205/09

  for tracking people · CPC title