Motion detection in mesh networks

• US 10,264,405 B1
• Filed: 05/01/2018
• Issued: 04/16/2019
• Est. Priority Date: 12/06/2017
• Status: Active Grant

• Current Assignee: Cognitive Systems Corp.
• Original Assignee: Cognitive Systems Corp.
• Inventors: Manku, Tajinder, Kravets, Oleksiy
• Primary Examiner(s): Sabouri, Mazda
• Application Number:
  US15/968,290
• Time in Patent Office: 350 Days
• CPC Class Codes: G01S 13/003 Bistatic radar systems; Multistatic radar systems
• CPC Class Codes: G01S 13/765 with exchange of information between interrogator and responder
• CPC Class Codes: G01S 13/886 for alarm systems alarms with electrical actuation G08B13/22
• CPC Class Codes: G01S 5/0284 Relative positioning
• CPC Class Codes: G08B 13/2494 by interference with electro-magnetic field distribution combined with other electrical sensor means, e.g. microwave detectors combined with other sensor means
• CPC Class Codes: H04W 4/029 Location-based management or tracking services
• CPC Class Codes: H04W 84/18 Self-organising networks, e.g. ad-hoc networks or sensor networks

Abstract

In a general aspect of the examples described, motion is detected based on received wireless signals in a mesh network. In an example, transfer function components for each pair of devices are obtained. A time series of transfer function matrices is generated, with each of the transfer function matrices comprising a respective subset of the transfer function components. Motion of an object in the space is detected based on the time series of transfer function matrices.

Claims

1. A method of motion detection in a mesh network, comprising:

obtaining transfer function components for each pair of devices in the mesh network, the transfer function components for each pair of devices based on wireless signals communicated through a space between the pair of devices;

generating a time series of transfer function matrices, each transfer function matrix comprising a respective subset of the transfer function components; and

detecting, by operation of one or more processors in a motion detection system, motion of an object in the space based on the time series of transfer function matrices.

2. The method of claim 1, wherein each subset of transfer function components comprises the transfer function components obtained for a certain transfer function matrix in the time series.

3. The method of claim 1, wherein each matrix position in each of the transfer function matrices represents a wireless link between one of the respective pairs of devices.

4. The method of claim 1, wherein each of the transfer function matrices is an M×

M matrix, and the mesh network comprises M wireless devices.

5. The method of claim 1, wherein detecting motion comprises processing the time series of transfer function matrices by operation of a neural network.

6. The method of claim 1, wherein detecting motion comprises detecting differences of transfer function components at one or more matrix positions over the time series.

7. The method of claim 6, wherein detecting differences of transfer function components comprises determining differences between transfer function components at adjacent points of the transfer function matrices in the time series.

8. A motion detection system comprising:

one or more processors;

memory storing instructions, which when executed by the one or more processors, cause the one or more processors to;

obtain transfer function components for each pair of devices in a mesh network, the transfer function components for each pair of devices based on wireless signals communicated through a space between the pair of devices;

generate a time series of transfer function matrices, each transfer function matrix comprising a respective subset of the transfer function components; and

detect motion of an object in the space based on the time series of transfer function matrices.

9. The motion detection system of claim 8, wherein each subset of transfer function components comprises the transfer function components obtained for a certain transfer function matrix in the time series.

10. The motion detection system of claim 8, wherein each matrix position in each of the transfer function matrices represents a wireless link between one of the respective pairs of devices.

11. The motion detection system of claim 8, wherein each of the transfer function matrices is an M×

M matrix, and the mesh network comprises M wireless devices.

12. The motion detection system of claim 8, wherein detecting motion comprises processing the time series of transfer function matrices by operation of a neural network.

13. The motion detection system of claim 8, wherein detecting motion comprises detecting differences of transfer function components at one or more matrix positions over the time series.

14. The motion detection system of claim 13, wherein detecting differences of transfer function components comprises determining differences between transfer function components at adjacent points of the transfer function matrices in the time series.

15. A non-transitory computer readable medium comprising instructions which when executed by one or more processors cause the one or more processors to:

obtain transfer function components for each pair of devices in a mesh network, the transfer function components for each pair of devices based on wireless signals communicated through a space between the pair of devices;

generate a time series of transfer function matrices, each transfer function matrix comprising a respective subset of the transfer function components; and

detect motion of an object in the space based on the time series of transfer function matrices.

16. The non-transitory computer readable medium of claim 15, wherein each subset of transfer function components comprises the transfer function components obtained for a certain transfer function matrix in the time series.

17. The non-transitory computer readable medium of claim 15, wherein each matrix position in each of the transfer function matrices represents a wireless link between one of the respective pairs of devices.

18. The non-transitory computer readable medium of claim 15, wherein each of the transfer function matrices is an M×

M matrix, and the mesh network comprises M wireless devices.

19. The non-transitory computer readable medium of claim 15, wherein detecting motion comprises processing the time series of transfer function matrices by operation of a neural network.

20. The non-transitory computer readable medium of claim 15, wherein detecting motion comprises detecting differences of transfer function components at one or more matrix positions over the time series.

21. The non-transitory computer readable medium of claim 20, wherein detecting differences of transfer function components comprises determining differences between transfer function components at adjacent points of the transfer function matrices in the time series.