Method and system of detecting dynamic status of a space

Patent metadata

Field	Value
Publication number	US-11749099-B2
Application number	US-202217568731-A
Country	US
Kind code	B2
Filing date	Jan 5, 2022
Priority date	Apr 23, 2021
Publication date	Sep 5, 2023
Grant date	Sep 5, 2023

Abstract

A detecting method for detecting a dynamic status in a space, wherein at least two wireless communication devices are deployed in the space and capable of performing a channel state detection to obtain a channel state information, the detecting method comprising: controlling the at least two wireless communication devices to perform the channel state detection in a registration stage to obtain a plurality of registration-stage channel state information; determining an environmental data of the space according to the plurality of registration-stage channel state information; controlling the at least two wireless communication devices to perform the channel state detection in a detection stage to obtain a plurality of detection-stage channel state information; and determining an intrusion situation of the space according to the environmental data and the plurality of detection-stage channel state information.

First claim

What is claimed is:

1. A detecting method for detecting a dynamic status in a space, wherein at least two wireless communication devices are deployed in the space and capable of performing a channel state detection to obtain a channel state information, the detecting method comprising:
controlling the at least two wireless communication devices to perform the channel state detection in a registration stage to obtain a plurality of registration-stage channel state information;
determining an environmental data of the space according to the plurality of registration-stage channel state information;
controlling the at least two wireless communication devices to perform the channel state detection in a detection stage to obtain a plurality of detection-stage channel state information; and
determining an intrusion situation of the space according to the environmental data and the plurality of detection-stage channel state information;
wherein the step of determining the environmental data of the space according to the plurality of registration-stage channel state information comprises:
using a principal component analysis to convert the plurality of registration-stage channel state information into a plurality of registration-stage principal component data; and
using a deep learning model to select a registration-stage principal component data from the plurality of registration-stage principal component data as the environmental data.

2. The detecting method of claim 1 , wherein a plurality of dynamic objects in the space have been moved out of the space in the registration stage.

3. The detecting method of claim 1 , wherein the step of converting the plurality of registration-stage channel state information into the plurality of registration-stage principal component data comprises excluding data with subcarrier amplitudes exceeding a threshold in the plurality of registration-stage channel state information.

4. The detecting method of claim 1 , wherein the step of using the deep learning model to select the registration-stage principal component data from the plurality of registration-stage principal component data as the environmental data comprises:
calculating a standard deviation of the plurality of registration-stage principal component data;
using the deep learning model to convert registration-stage principal component data with standard deviations less than a threshold in the plurality of registration-stage principal component data into a plurality of neural network embedded vectors, wherein an amount of the plurality of neural network embedded vectors is greater than a default value;
respectively calculating an average in-class degree of each neural network embedded vector of the plurality of neural network embedded vectors relative to other neural network embedded vectors; and
selecting the registration-stage principal component data corresponding to the neural network embedded vector with a largest average in-class degree in the plurality of neural network embedded vectors as the environmental data.

5. The detecting method of claim 1 , wherein the step of determining the intrusion situation of the space according to the environmental data and the plurality of detection-stage channel state information comprises:
using the principal component analysis to convert the plurality of detection-stage channel state information into a plurality of detection-stage principal component data;
using the deep learning model to analyze the environmental data and the plurality of detection-stage principal component data, to classify an environmental state of the space into a plurality of categories;
determining that there is at least one intruder in the space, when one of the plurality of detection-stage principal component data falls within an intrusion category of the plurality of categories.

6. The detecting method of claim 5 , wherein the step of converting the plurality of detection-stage channel state information into the plurality of detection-stage principal component data comprises excluding detection-stage channel state information with subcarrier amplitudes exceeding a threshold in the plurality of detection-stage channel state information, and converting the remaining detection-stage channel state information into the plurality of detection-stage principal component data.

7. The detecting method of claim 5 , further comprising generating a warning signal after determining that there is at least one intruder in the space.

8. The detecting method of claim 1 , further comprising training the deep learning model with the plurality of detection-stage channel state information.

9. A detecting system for detecting a dynamic status in a space, comprising:
at least two wireless communication devices, deployed in the space and capable of performing a channel state detection to obtain a channel state information; and
a computing device, comprising:
a processing unit, configured to execute a program code; and
a storage unit, coupled to the processing unit to store the program code, wherein the program code is configured to instruct the processing unit to execute the following steps:
controlling the at least two wireless communication devices to perform the channel state detection in a registration stage to obtain a plurality of registration-stage channel state information;
determining an environmental data of the space according to the plurality of registration-stage channel state information;
controlling the at least two wireless communication devices to perform the channel state detection in a detection stage to obtain a plurality of detection-stage channel state information; and
determining an intrusion situation of the space according to the environmental data and the plurality of detection-stage channel state information;

wherein the step of determining the environmental data of the space according to the plurality of registration-stage channel state information comprises:
using a principal component analysis to convert the plurality of registration-stage channel state information into a plurality of registration-stage principal component data; and
using a deep learning model to select a registration-stage principal component data from the plurality of registration-stage principal component data as the environmental data.

10. The detecting system of claim 9 , wherein a plurality of dynamic objects in the space have been moved out of the space in the registration stage.

11. The detecting system of claim 9 , wherein the step of converting the plurality of registration-stage channel state information into the plurality of registration-stage principal component data comprises excluding data with subcarrier amplitudes exceeding a threshold in the plurality of registration-stage channel state information.

12. The detecting system of claim 9 , wherein the step of using the deep learning model to select the registration-stage principal component data from the plurality of registration-stage principal component data as the environmental data comprises:
calculating a standard deviation of the plurality of registration-stage principal component data;
using the deep learning model to convert registration-stage principal component data with standard deviations less than a threshold in the plurality of registration-stage principal component data into a plurality of neural network embedded vectors, wherein an amount of the plurality of neural network embedded vectors is greater than a default value;
respectively calculating an average in-class degree of each neural network embedded vector of the plurality of neural network embedded vectors relative to other neural ne

Assignees

• Wistron Neweb Corp

Inventors

• Chen Yi-An
• Huang Wang-Hsing
• Cheng Chui-Chu

Classifications

• G08B29/186 Primary

  Fuzzy logic; neural networks · CPC title

• G08B13/22

  Electrical actuation · CPC title

• H04B17/309

  Measuring or estimating channel quality parameters · CPC title

• H04W60/00

  Affiliation to network, e.g. registration; Terminating affiliation with the network, e.g. de-registration · CPC title

• H04W24/10

  Scheduling measurement reports {; Arrangements for measurement reports} · CPC title