# US 10,375,667 B2 · Enhancing indoor positioning using RF multilateration and optical sensing

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- Number: US 10,375,667 B2
- Document ID: US10375667B2
- Status: Granted
- Kind: B2
- Application number: 15/834,990
- Filed: 2017-12-07
- Published: 2019-06-13
- Granted: 2019-08-06
- Earliest claimed: 2017-12-07
- Assignees: Cisco Technology, Inc.
- Applicants: Cisco Technology, Inc.
- Inventors: Shanay Ravin Kothari; Ian Matthew Snyder
- Web page: https://patentgrep.com/patent/US10375667B2

## CPC

- H04W 64/00: Locating users or terminals {or network equipment} for network management purposes, e.g. mobility management
- G01S 5/0252: {Radio frequency fingerprinting}
- G01S 5/0264: {at least one of the systems being a non-radio wave positioning system}
- G01S 5/14: Determining absolute distances from a plurality of spaced points of known location
- G06T 7/20: Analysis of motion (motion estimation for coding, decoding, compressing or decompressing digital video signals H04N19/43, H04N19/51)
- G06V 20/52: Surveillance or monitoring of activities, e.g. for recognising suspicious objects (recognising microscopic objects G06V20/69)
- H04L 67/10: in which an application is distributed across nodes in the network (software deployment G06F8/60; multiprogramming arrangements G06F9/46)
- H04W 88/085: {Access point devices with remote components}
- H04B 7/0857: {using maximum ratio combining techniques, e.g. signal-to- interference ratio [SIR], received signal strenght indication [RSS]}
- H04L 67/52: specially adapted for the location of the user terminal
- H04N 7/18: Closed-circuit television [CCTV] systems, i.e. systems in which the video signal is not broadcast
- H04W: WIRELESS COMMUNICATION NETWORKS (broadcast communication H04H; communication systems using wireless links for non-selective communication, e.g. wireless extensions H04M1/72)
- G01S: RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- H04L: TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION (arrangements common to telegraphic and telephonic communication H04M)
- H04B: TRANSMISSION
- H04N: PICTORIAL COMMUNICATION, e.g. TELEVISION

## Abstract

Systems and methods are disclosed for enhancing target positioning. A broadcast signal from a device is received by at one or more access points, where a first position area of the device is determined from an analysis of the broadcast signal. A second position area of the target, which is within the first position area, is determined by scanning pixels within the first position area in an image captured by the camera and, based on the scanned pixels, at least one target comprising a portion of the pixels is detected. The target within the image is classified, and based on the classification and portion of the pixels comprising the target, the second position area of the target within the first position of the image is triangulated.

## Claims

### Claim 1

A system for enhancing target positioning comprising:
an access point for capturing a broadcast signal from a device, wherein a first position area of the device is determined by analyzing the broadcast signal; and
a camera in communication with the access point to receive the first position area from the access point, the camera including computer readable instructions stored thereon that are effective to:
scan pixels within the received first position area in an image captured by the camera;
based on the scanned pixels, detect at least one target within a portion of the pixels, wherein the target can be comprised of at least one of the device or a person associated with the device;
classify the target within the image; and
based on the classification and portion of the pixels including the target, triangulate a second position area of the at least one target within the first position area of the image.

### Claim 2 (depends on 1)

The system of claim 1, wherein at least two access points capture the broadcast signal from the device, and wherein the first position area of the device is determined by analyzing the broadcast signal according to a multilateration model.

### Claim 3 (depends on 1)

The system of claim 1, wherein the classification of the target further comprises instructions to:
identify a broadcast signature within the broadcast signal; and
identify a type of the device based on comparing the broadcast signature with a plurality of stored broadcast signatures with associated device information.

### Claim 4 (depends on 1)

The system of claim 1, wherein the classification of the target further comprises instructions to:
identify a type of the device based on machine vision applied to at least one of the first position area or the second position area.

### Claim 5 (depends on 3)

The system of claim 3, wherein an identifier that identifies the vendor of the device is determined from the broadcast signature, and wherein the type of the device is determined from the vendor.

### Claim 6 (depends on 1)

The system of claim 1, the instructions effective to:
identify the device based on an application installed on the device, wherein the application is associated with a user assigned to the device;
classify the target in the second area as a first person; and
based on machine vision applied to the first person, determine that there is a discrepancy in possession of the device by identifying that the first person is not the user assigned to the device.

### Claim 7 (depends on 1)

The system of claim 1, the instructions effective to:
receive a broadcast signature within the broadcast signal from the first position area;
identify a type of the device based on machine vision applied to the first position area; and
update a broadcast signature store based on the received broadcast signature and identified type of device.

### Claim 8 (depends on 7)

The system of claim 7, wherein the updated broadcast signature dynamically updates a multilateration model.

### Claim 9 (depends on 1)

The system of claim 1, the instructions effective to:
identify, based on the broadcast signal captured by the access point, that a plurality of devices associated with a plurality of targets are within the first position area;
determine that at least one device within the plurality of devices cannot be visually differentiated by machine vision;
track movement of the plurality of targets within the image as a cohesive block; and
apply a differencing model to the broadcast signal to extract broadcast signals for each device.

### Claim 10 (depends on 1)

The system of claim 1, the instructions effective to:
identify, based on the broadcast signal captured by the access point, that a plurality of devices associated with a plurality of targets are within the first position area;
determine that at least one device within the plurality of devices cannot be visually differentiated using a machine vision technique;
track movement of the plurality of targets within the image;
determine, using the machine vision technique, that at least one target within the plurality of targets is moving in a direction that diverges from the plurality of targets;
for the at least one target, visually identify the associated device; and
remove, from the broadcast signal, the broadcast signature associated with the visually identified device.

### Claim 11 (depends on 1)

The system of claim 1, the instructions effective to:
identify, based on the broadcast signal captured by the access point, that a plurality of devices associated with a plurality of targets are within the first position area;
determine that at least one device within the plurality of devices cannot be visually differentiated using a machine vision technique;
based on no diverging movement within the plurality of targets, assign all broadcast signals a group identifier and average the broadcast signal.

### Claim 12 (depends on 1)

The system of claim 1, the instructions effective to:
determine that a machine vision technique cannot detect the device associated with the target;
track the target; and
when the device is detectable using the machine vision technique, identify the device.

### Claim 13 (depends on 1)

The system of claim 1, wherein the classification of the target is based on a model in the cloud.

### Claim 14

A method of enhancing target positioning comprising:
receiving, from an access point, a broadcast signal from a device, wherein a first position area of the device is determined from an analysis of the broadcast signal;
determining a second position area of the target within the first position area by:
scanning pixels within the received first position area in an image captured by the camera;
based on the scanned pixels, detecting at least one target comprising a portion of the pixels, wherein the target can be comprised of at least one of the device or a person associated with the device;
classifying the target within the image; and
based on the classification and portion of the pixels comprising the target, triangulating the second position area of the target within the first position area of the image.

### Claim 15 (depends on 14)

The method of claim 14, wherein receiving the broadcast signal comprises capturing the broadcast signal from the device with at least two access points, and wherein the first position area of the device is determined by analyzing the broadcast signal according to a multilateration model.

### Claim 16 (depends on 14)

The method of claim 14, the method further comprising:
identifying the device based on an application installed on the device, wherein the application is associated with a user assigned to the device;
classifying the target in the second area as a first person; and
based on machine vision applied to the first person, determining that there is a discrepancy in possession of the device by identifying that the first person is not the user assigned to the device.

### Claim 17 (depends on 14)

The method of claim 14, the method further comprising:
identifying, based on the broadcast signal, that a plurality of devices associated with a plurality of targets are within the first position area;
determining that at least one device within the plurality of devices cannot be visually differentiated by machine vision;
tracking movement of the plurality of targets within the image;
determining, by machine vision, that at least one target within the plurality of targets is moving in a direction that diverges from the plurality of targets;
for the at least one target, visually identifying the associated device; and
removing, from the broadcast signal, the broadcast signature associated with the visually identified device.

### Claim 18

A camera comprising:
at least one receiver for receiving, from an access point, a broadcast signal from a device, wherein a first position area of the device is determined from an analysis of the broadcast signal; and
at least one processor and at least one memory containing instructions that, when executed, cause the at least one processor to determine a second position area of the target within the first position area by:
scanning pixels within the received first position area in an image captured by the camera;
based on the scanned pixels, detecting at least one target comprising a portion of the pixels, wherein the target can be comprised of at least one of the device or a person associated with the device;
classifying the target within the image; and
based on the classification and portion of the pixels comprising the target, triangulating the second position area of the target within the first position of the image.

### Claim 19 (depends on 18)

The camera of claim 18, wherein at least two access points capture the broadcast signal from the device, and wherein the first position area of the device is determined by analyzing the broadcast signal according to a multilateration model.

### Claim 20 (depends on 18)

The camera of claim 18, wherein the at least one processor is further configured to:
identify the device based on an application installed on the device, wherein the application is associated with a user assigned to the device;
classify the target in the second area as a first person; and
based on machine vision applied to the first person, determine that there is a discrepancy in possession of the device by identifying that the first person is not the user assigned to the device.