Method, apparatus, and system for accurate wireless monitoring

Patent metadata

Field	Value
Publication number	US-11770197-B2
Application number	US-202017113023-A
Country	US
Kind code	B2
Filing date	Dec 5, 2020
Priority date	Jul 17, 2015
Publication date	Sep 26, 2023
Grant date	Sep 26, 2023

Abstract

Methods, apparatus and systems for improving accuracy and efficiency of wireless monitoring are described. In one example, a described method comprises: transmitting a first wireless signal from a first wireless device with transmit antennas through a wireless multipath channel of a venue; receiving a second wireless signal by a second wireless device with receive antennas 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 motion of an object in the venue; obtaining a number of time series of channel information (TSCI) of the wireless multipath channel based on the second wireless signal, wherein each TSCI is associated with a respective one of the transmit antennas and a respective one of the receive antennas; preprocessing the number of TSCI; and monitoring the motion of the object based on the number of preprocessed TSCI.

First claim

We claim:

1. A method for wireless monitoring, comprising:
transmitting a first wireless signal from a first wireless device with N1 transmit antennas through a wireless multipath channel of a venue, wherein N1 is a positive integer larger than one;
receiving a second wireless signal by a second wireless device with N2 receive antennas 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 motion of an object in the venue, wherein N2 is a positive integer larger than one;
extracting a number of time series of channel information (TSCI) of the wireless multipath channel based on the second wireless signal using a processor, a memory communicatively coupled with the processor and a set of instructions stored in the memory, wherein each TSCI is associated with a respective one of the N1 transmit antennas and a respective one of the N2 receive antennas;
computing a testing measure of fluctuation of a characteristic of channel information (CI) for a time window covering at least part of the number of TSCI;
computing an adaptive threshold of the time window based on: (a) the number of TSCI having a first quantity of CI and (b) at least one of:
carrier frequency of the second wireless signal,
bandwidth of the second wireless signal,
timing of the second wireless signal,
signal strength of the second wireless signal,
sounding rate of the second wireless signal;

preprocessing the number of TSCI that has been extracted out from the second wireless signal, wherein the number of TSCI is preprocessed by removing a plurality of CI in the time window when the testing measure of fluctuation of the characteristic of at least one of the plurality of CI in the time window is larger than the adaptive threshold of the time window, to reduce a total quantity of CI in the number of TSCI from the first quantity to a second quantity, wherein the adaptive threshold is updated based on the second quantity;
computing a similarity score between a pair of temporally adjacent CI of a TSCI in the number of preprocessed TSCI;
computing an autocorrelation function (ACF) of the TSCI;
computing at least one characteristic point of the ACF; and
monitoring the motion of the object in the venue based on: the number of preprocessed TSCI, the ACF, the at least one characteristic point of the ACF, and the similarity score.

2. The method of claim 1 , wherein:
the number of TSCI are preprocessed to remove, among the number of TSCI, channel information (CI) whose quality is lower than a predetermined threshold.

3. The method of claim 1 , wherein the preprocessing comprises:
identifying the time window as a questionable time window which is at least one of: abnormal, atypical, irregular, untrustworthy, questionable, or erratic;
identifying all channel information (CI) in the questionable time window as questionable CI; and
computing the preprocessed TSCI by removing all questionable CI from the number of TSCI.

4. The method of claim 3 , wherein:
the questionable time window has zero time duration.

5. The method of claim 3 , wherein:
the questionable time window comprises a single time stamp.

6. The method of claim 1 , wherein the characteristic of CI is related to at least one of:
differences between two CI at adjacent time stamps in the time window;
a function of the differences;
amplitudes of CI at consecutive time stamps in the time window; or
a function of the amplitudes.

7. The method of claim 6 , wherein the testing measure is computed based on at least one of:
sum, difference, multiplication, division, mean, weighted average, trimmed mean, L-k norm, L-k distance, statistics, median, ordered statistics, measure of variation, deviation, variance,
slope, derivative, partial derivative, scalar, vector, magnitude, phase, absolute value,
maximum, minimum, zero-crossing, thresholding,
moving function, sliding function, transformation, normalization, projection, decomposition, classification, filtering, or sampling.

8. The method of claim 6 , wherein the preprocessing further comprises:
computing a plurality of instantaneous testing measures, wherein:
the time window comprises a plurality of time stamps,
each of the instantaneous testing measures is associated with a respective one of the time stamps in the time window, and
the testing measure is computed based on an aggregation of the plurality of instantaneous testing measures.

9. The method of claim 8 , wherein the plurality of instantaneous testing measures are aggregated based on at least one of:
sum, difference, multiplication, division, mean, weighted average, trimmed mean,
L-k norm, L-k distance, statistics, median, ordered statistics, measure of variation, deviation, variance,
slope, derivative, partial derivative, scalar, vector, magnitude, phase, absolute value,
maximum, minimum, zero-crossing, thresholding,
moving function, sliding function, transformation, normalization, projection, decomposition, classification, filtering, or sampling.

10. The method of claim 6 ,
wherein the time window is identified as the questionable time window when the testing measure exceeds the adaptive threshold.

11. The method of claim 10 , wherein the adaptive threshold is computed based on at least one of:
sum, difference, multiplication, division, mean, weighted average, trimmed mean,
L-k norm, L-k distance, statistics, median, ordered statistics, measure of variation, deviation, variance
slope, derivative, partial derivative, scalar, vector, magnitude, phase, absolute value,
maximum, minimum, zero-crossing, thresholding,
moving function, sliding function, transformation, normalization, projection, decomposition, classification, filtering, or sampling.

12. The method of claim 10 , wherein the adaptive threshold is computed further based on at least one of:
an amount of antennas of the first wireless device,
an amount of antennas of the second wireless device,
co-location of the first wireless device and the second wireless device,
protocol between the first wireless device and the second wireless device,
bandwidth of the wireless multipath channel,
amount of the CI,
the monitoring of the motion of the object, or
a task associated with the monitoring.

13. The method of claim 1 , further comprising:
computing at least one time series of features (TSF) based on the number of TSCI, wherein the motion of the object is monitored based on the at least one TSF.

14. The method of claim 13 , wherein the at least one TSF is computed based on at least one of:
sum, difference, multiplication, division, mean, weighted average, trimmed mean,
L-k norm, L-k distance, statistics, median, ordered statistics, measure of variation, deviation, variance,
slope, derivative, partial derivative, scalar, vector, magnitude, phase, absolute value,
maximum, minimum, zero-crossing, thresholding,
moving function, sliding function, transformation, normalization, projection, decomposition, classification, filtering, sampling,
an amount of antennas of the first wireless device,
an amount of antennas of the second wireless device,
co-location of the first wireless device and the second wireless device, or
the monitoring of the motion of the object.

15. The method of claim 13 , wherein each feature of the at least one TSF is resistant, insensitive, or invariant, with respect to at least one of:
phase noise, phase distortion, phase imperfection, phase offset, phase error,
amplitude noise, amplitude distortion, amplitude imperfecti

Assignees

• Zhang Feng
• Wang Beibei
• Lai Hung Quoc Duc
• Au Oscar Chi Lim
• Liu K J Ray
• Cao Yexin
• Origin Wireless Inc

Inventors

• Zhang Feng
• Wang Beibei
• Lai Hung-Quoc Duc
• Au Oscar Chi-Lim
• Liu K J Ray
• Cao Yexin

Classifications

• H04B7/0626 Primary

  Channel coefficients, e.g. channel state information [CSI] · CPC title

• H04B17/309 Primary

  Measuring or estimating channel quality parameters · CPC title