Determining a location of motion detected from wireless signals based on wireless link counting

• US 10,459,074 B1
• Filed: 04/30/2019
• Issued: 10/29/2019
• Est. Priority Date: 04/30/2019
• Status: Active Grant

• Current Assignee: Cognitive Systems Corp.
• Original Assignee: Cognitive Systems Corp.
• Inventors: Omer, Mohammad, Devison, Stephen Arnold, Kravets, Oleksiy
• Primary Examiner(s): Magloire, Vladimir
• Assistant Examiner(s): Seraydaryan, Helena H
• Application Number:
  US16/399,657
• Time in Patent Office: 182 Days
• Field of Search: 342126
• CPC Class Codes: G01S 11/023 using impedance elements varying with distance
• CPC Class Codes: G01S 11/06 using intensity measurements
• CPC Class Codes: G01S 13/003 Bistatic radar systems; Multistatic radar systems
• CPC Class Codes: G01S 13/50 Systems of measurement based on relative movement of target
• CPC Class Codes: G01S 5/0278 involving statistical or probabilistic considerations G01S5/0252, G01S5/0294 take precedence
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 7/01 Probabilistic graphical models, e.g. probabilistic networks
• CPC Class Codes: H04W 4/02 Services making use of location information
• CPC Class Codes: H04W 64/006 with additional information processing, e.g. for direction or speed determination

Abstract

In a general aspect, a method for determining a location of motion detected by wireless communication devices in a wireless communication network includes obtaining a set of motion indicator values associated with a time frame. The set of motion indicator values indicates motion detected from wireless links during the time frame, each motion indicator value associated with a respective wireless link. The method also includes identifying a subset of the wireless links based on a magnitude of their respective motion indicator values relative to other motion indicator values in the set of motion indicator values. The method additionally includes generating count values for the wireless communication devices connected to the wireless communication network during the time frame. The method further includes generating a probability vector based on the count values and comprising values for the connected wireless communication devices.

Claims

1. A method comprising:

obtaining a set of motion indicator values associated with a time frame, the set of motion indicator values indicating motion detected from wireless links in a wireless communication network during the time frame, each motion indicator value associated with a respective wireless link, each wireless link defined between a respective pair of wireless communication devices in the wireless communication network;

identifying a subset of the wireless links based on a magnitude of their respective motion indicator values relative to other motion indicator values in the set of motion indicator values;

generating count values for the wireless communication devices connected to the wireless communication network during the time frame, the count value for each wireless communication device indicating how many wireless links in the identified subset are defined by the wireless communication device; and

generating a probability vector based on the count values and comprising values for the connected wireless communication devices, the value for each connected wireless communication device representing a probability of motion at the connected wireless communication device during the time frame.

2. The method of claim 1, comprising:

altering one or more magnitudes of the set of motion indicator values to reference each motion indicator value to a common scale of wireless link sensitivity.

3. The method of claim 1, comprising:

identifying wireless links active in the wireless communication network during the time frame based on the set of motion indicator values.

4. The method of claim 3, comprising:

identifying pairs of wireless communication devices that define each identified active wireless link, thereby identifying wireless communication devices connected to the wireless communication network during the time frame.

5. The method of claim 1,

wherein the time frame is a subsequent time frame that is after a prior time frame, the probability vector is a first probability vector, and the values are first values;

and

wherein the method comprises;

obtaining a second probability vector generated from motion indicator values associated with the prior time frame, the second probability vector comprising second values for wireless communication devices connected to the wireless communication network during the prior time frame, the second values representing probabilities of motion at the connected wireless communication devices during the prior time frame,

generating a third probability vector based on the first values of the first probability vector and the second values of the second probability vector, the third probability vector comprising third values for the wireless communication devices connected to the wireless communication network during the subsequent time frame, the third values representing probabilities of motion at the connected wireless communication devices during the subsequent time frame,

identifying a wireless communication device associated with the highest of the third values, and

identifying, by operation of a data processing apparatus, a location associated with the identified wireless communication device as a location of the motion detected from the wireless signals exchanged during the subsequent time frame.

6. The method of claim 5, comprising:

obtaining a transition probability matrix comprising;

transition values representing probabilities of motion for transitioning between locations associated with distinct wireless communication devices, and

non-transition values representing probabilities of motion for remaining within locations associated with the respective wireless communication devices; and

wherein the generating the third probability vector comprises generating the third probability vector based on the first values of the first probability vector, the second values of the second probability vector, and the transition and non-transition values of the transition probability matrix.

7. The method of claim 5, comprising:

repeating the operations, over multiple iterations for respective time frames, of obtaining the set of motion indicator values, identifying the subset of the wireless links, generating the count values, generating the first probability vector, obtaining the second probability vector, generating the third probability vector, identifying the wireless communication device, and identifying the location; and

wherein the third probability vector of a previous iteration serves as the second probability vector of a present iteration, thereby allowing the third probability vector to be recursively updated.

8. A system comprising:

wireless communication devices in a wireless communication network that are configured to exchange wireless signals over wireless links, each wireless link defined between a respective pair of the wireless communication devices; and

one or more processors; and

memory storing instructions that are configured to perform operations when executed by the one or more processors, the operations comprising;

obtaining a set of motion indicator values associated with a time frame, the set of motion indicator values indicating motion detected from the wireless links during the time frame, each motion indicator value associated with a respective wireless link,

identifying a subset of the wireless links based on a magnitude of their respective motion indicator values relative to other motion indicator values in the set of motion indicator values,

generating count values for the wireless communication devices connected to the wireless communication network during the time frame, the count value for each wireless communication device indicating how many wireless links in the identified subset are defined by the wireless communication device, and

generating a probability vector based on the count values and comprising values for the connected wireless communication devices, the value for each connected wireless communication device representing a probability of motion at the connected wireless communication device during the time frame.

9. The system of claim 8, the operations comprising:

altering one or more magnitudes of the set of motion indicator values to reference each motion indicator value to a common scale of wireless link sensitivity.

10. The system of claim 8, the operations comprising:

identifying wireless links active in the wireless communication network during the time frame based on the set of motion indicator values.

11. The system of claim 10, the operations comprising:

identifying pairs of wireless communication devices that define each identified active wireless link, thereby identifying wireless communication devices connected to the wireless communication network during the time frame.

12. The system of claim 8,

wherein the time frame is a subsequent time frame that is after a prior time frame, the probability vector is a first probability vector, and the values are first values;

and

wherein the operations comprise;

obtaining a second probability vector generated from motion indicator values associated with the prior time frame, the second probability vector comprising second values for wireless communication devices connected to the wireless communication network during the prior time frame, the second values representing probabilities of motion at the connected wireless communication devices during the prior time frame,

generating a third probability vector based on the first values of the first probability vector and the second values of the second probability vector, the third probability vector comprising third values for the wireless communication devices connected to the wireless communication network during the subsequent time frame, the third values representing probabilities of motion at the connected wireless communication devices during the subsequent time frame,

identifying a wireless communication device associated with the highest of the third values, and

identifying, by operation of a data processing apparatus, a location associated with the identified wireless communication device as a location of the motion detected from the wireless signals exchanged during the subsequent time frame.

13. The system of claim 12, the operations comprising:

obtaining a transition probability matrix comprising;

transition values representing probabilities of motion for transitioning between locations associated with distinct wireless communication devices, and

non-transition values representing probabilities of motion for remaining within locations associated with the respective wireless communication devices; and

wherein the generating the third probability vector comprises generating the third probability vector based on the first values of the first probability vector, the second values of the second probability vector, and the transition and non-transition values of the transition probability matrix.

14. The system of claim 12, the operations comprising:

repeating the operations, over multiple iterations for respective time frames, of obtaining the set of motion indicator values, identifying the subset of the wireless links, generating the count values, generating the first probability vector, obtaining the second probability vector, generating the third probability vector, identifying the wireless communication device, and identifying the location; and

wherein the third probability vector of a previous iteration serves as the second probability vector of a present iteration, thereby allowing the third probability vector to be recursively updated.

15. The system of claim 8, wherein at least one of the wireless communication devices comprises the one or more processors and the memory.

16. A non-transitory computer-readable medium storing instructions that, when executed by data processing apparatus, cause the data processing apparatus to perform operations comprising:

obtaining a set of motion indicator values associated with a time frame, the set of motion indicator values indicating motion detected from wireless links in a wireless communication network during the time frame, each motion indicator value associated with a respective wireless link, each wireless link defined between a respective pair of wireless communication devices in the wireless communication network;

identifying a subset of the wireless links based on a magnitude of their respective motion indicator values relative to other motion indicator values in the set of motion indicator values;

generating count values for the wireless communication devices connected to the wireless communication network during the time frame, the count value for each wireless communication device indicating how many wireless links in the identified subset are defined by the wireless communication device; and

generating a probability vector based on the count values and comprising values for the connected wireless communication devices, the value for each connected wireless communication device representing a probability of motion at the connected wireless communication device during the time frame.

17. The computer-readable medium of claim 16, comprising:

altering one or more magnitudes of the set of motion indicator values to reference each motion indicator value to a common scale of wireless link sensitivity.

18. The computer-readable medium of claim 16, comprising:

identifying wireless links active in the wireless communication network during the time frame based on the set of motion indicator values.

19. The computer-readable medium of claim 18, comprising:

identifying pairs of wireless communication devices that define each identified active wireless link, thereby identifying wireless communication devices connected to the wireless communication network during the time frame.

20. The computer-readable medium of claim 16,

wherein the time frame is a subsequent time frame that is after a prior time frame, the probability vector is a first probability vector, and the values are first values;

and

wherein the operations comprise;

obtaining a second probability vector generated from motion indicator values associated with the prior time frame, the second probability vector comprising second values for wireless communication devices connected to the wireless communication network during the prior time frame, the second values representing probabilities of motion at the connected wireless communication devices during the prior time frame,

generating a third probability vector based on the first values of the first probability vector and the second values of the second probability vector, the third probability vector comprising third values for the wireless communication devices connected to the wireless communication network during the subsequent time frame, the third values representing probabilities of motion at the connected wireless communication devices during the subsequent time frame,

identifying a wireless communication device associated with the highest of the third values, and

identifying, by operation of a data processing apparatus, a location associated with the identified wireless communication device as a location of the motion detected from the wireless signals exchanged during the subsequent time frame.

21. The computer-readable medium of claim 20, comprising:

obtaining a transition probability matrix comprising;

transition values representing probabilities of motion for transitioning between locations associated with distinct wireless communication devices, and

non-transition values representing probabilities of motion for remaining within locations associated with the respective wireless communication devices; and

wherein the generating the third probability vector comprises generating the third probability vector based on the first values of the first probability vector, the second values of the second probability vector, and the transition and non-transition values of the transition probability matrix.

22. The computer-readable medium of claim 20, comprising:

repeating the operations, over multiple iterations for respective time frames, of obtaining the set of motion indicator values, identifying the subset of the wireless links, generating the count values, generating the first probability vector, obtaining the second probability vector, generating the third probability vector, identifying the wireless communication device, and identifying the location; and

wherein the third probability vector of a previous iteration serves as the second probability vector of a present iteration, thereby allowing the third probability vector to be recursively updated.