Wireless receive signal strength indicator (RSSI)-based positioning

Patent metadata

Field	Value
Publication number	US-12265167-B2
Application number	US-202217930644-A
Country	US
Kind code	B2
Filing date	Sep 8, 2022
Priority date	Jun 27, 2022
Publication date	Apr 1, 2025
Grant date	Apr 1, 2025

Abstract

A method includes receiving wireless fingerprint data identifying multiple locations within a specified area and, for each location, one or more signal strength values associated with wireless signals received from one or more of multiple wireless transmitters. The wireless fingerprint data is missing signal strength values for one or more transmitters at one or more specific locations. The method also includes generating a training dataset by adding filler signal strength values in place of at least some missing values. The method further includes training a machine learning model using the training dataset. The model is trained to receive a specified location as input and generate predicted signal strength values as outputs. In addition, the method includes using the trained model to generate additional signal strength values. At least some additional signal strength values are to be used in place of at least a portion of the missing values.

First claim

What is claimed is:

1. A method comprising:
receiving wireless fingerprint data associated with a specified area, the wireless fingerprint data identifying (i) multiple locations within the specified area and (ii) for each location, one or more signal strength values associated with wireless signals received at the location from one or more of multiple wireless transmitters, wherein the wireless fingerprint data is missing signal strength values for one or more of the wireless transmitters at one or more specific locations within the specified area;
generating a first training dataset by adding filler signal strength values in place of at least some of the signal strength values that are missing from the wireless fingerprint data;
training a first machine learning model using the first training dataset, the first machine learning model trained to receive a specified location as input and generate predicted signal strength values as outputs; and
using the trained first machine learning model to generate additional signal strength values, at least some of the additional signal strength values to be used in place of at least a portion of the signal strength values that are missing from the wireless fingerprint data.

2. The method of claim 1 , wherein:
the trained first machine learning model is used to generate a second training dataset, the second training dataset including the additional signal strength values generated using the trained first machine learning model; and
the method further comprises training a second machine learning model using the second training dataset, the second machine learning model trained to receive input signal strength values and generate a predicted location based on the input signal strength values.

3. The method of claim 2 , further comprising:
deploying the trained second machine learning model to a mobile electronic device for use in predicting a location of the mobile electronic device based on wireless signals received by the mobile electronic device.

4. The method of claim 2 , wherein:
the first machine learning model comprises a neural network; and
the second machine learning model comprises a weighed K-nearest neighbor model.

5. The method of claim 1 , wherein training the first machine learning model comprises minimizing an error between (i) the signal strength values and the filler signal strength values contained in the first training dataset and (ii) predicted signal strength values generated by the first machine learning model during the training.

6. The method of claim 1 , wherein training the first machine learning model comprises validating performance of the first machine learning model using a masked loss that is based on at least some of the signal strength values contained in the wireless fingerprint data.

7. The method of claim 1 , wherein each filler signal strength value is (i) associated with location coordinates within the specified area and (ii) determined using one or more signal strength values that are within a threshold distance of the location coordinates.

8. An apparatus comprising:
at least one processing device configured to:
receive wireless fingerprint data associated with a specified area, the wireless fingerprint data identifying (i) multiple locations within the specified area and (ii) for each location, one or more signal strength values associated with wireless signals received at the location from one or more of multiple wireless transmitters, wherein the wireless fingerprint data is missing signal strength values for one or more of the wireless transmitters at one or more specific locations within the specified area;
generate a first training dataset by adding filler signal strength values in place of at least some of the signal strength values that are missing from the wireless fingerprint data;
train a first machine learning model using the first training dataset, the first machine learning model trained to receive a specified location as input and generate predicted signal strength values as outputs; and
use the trained first machine learning model to generate additional signal strength values, at least some of the additional signal strength values to be used in place of at least a portion of the signal strength values that are missing from the wireless fingerprint data.

9. The apparatus of claim 8 , wherein:
the at least one processing device is configured to use the trained first machine learning model to generate a second training dataset, the second training dataset including the additional signal strength values generated using the trained first machine learning model; and
the at least one processing device is further configured to train a second machine learning model using the second training dataset, the second machine learning model trained to receive input signal strength values and generate a predicted location based on the input signal strength values.

10. The apparatus of claim 9 , wherein the at least one processing device is further configured to deploy the trained second machine learning model to a mobile electronic device for use in predicting a location of the mobile electronic device based on wireless signals received by the mobile electronic device.

11. The apparatus of claim 9 , wherein:
the first machine learning model comprises a neural network; and
the second machine learning model comprises a weighed K-nearest neighbor model.

12. The apparatus of claim 8 , wherein, to train the first machine learning model, the at least one processing device is configured to minimize an error between (i) the signal strength values and the filler signal strength values contained in the first training dataset and (ii) predicted signal strength values generated by the first machine learning model during the training.

13. The apparatus of claim 8 , wherein, to train the first machine learning model, the at least one processing device is configured to validate performance of the first machine learning model using a masked loss that is based on at least some of the signal strength values contained in the wireless fingerprint data.

14. The apparatus of claim 8 , wherein each filler signal strength value is (i) associated with location coordinates within the specified area and (ii) determined using one or more signal strength values that are within a threshold distance of the location coordinates.

15. A non-transitory computer readable medium containing instructions that when executed cause at least one processor to:
receive wireless fingerprint data associated with a specified area, the wireless fingerprint data identifying (i) multiple locations within the specified area and (ii) for each location, one or more signal strength values associated with wireless signals received at the location from one or more of multiple wireless transmitters, wherein the wireless fingerprint data is missing signal strength values for one or more of the wireless transmitters at one or more specific locations within the specified area;
generate a first training dataset by adding filler signal strength values in place of at least some of the signal strength values that are missing from the wireless fingerprint data;
train a first machine learning model using the first training dataset, the first machine learning model trained to receive a specified location as input and generate predicted signal strength values as outputs; and
use the trained first machine learning model to generate additional signal strength values, at least some of the additional signal strength values to be used in place of at least a portion of the signal strength values t

Assignees

• Samsung Electronics Co Ltd

Inventors

• Xing Tianwei
• Jiang Wenjun
• Chen Xun

Classifications

• G01S5/0278

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

• G01S5/02528

  Simulating radio frequency fingerprints · CPC title

• H04W64/003 Primary

  locating network equipment · CPC title

• G01S5/02521 Primary

  using a radio-map · CPC title