# US 2019/0173594 A1 · System and method for wireless location

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- Number: US 2019/0173594 A1
- Document ID: US20190173594A1
- Status: Published application
- Kind: A1
- Application number: 16/323,467
- Filed: 2017-08-07
- Published: 2019-06-06
- Earliest claimed: 2016-08-05
- Assignees: Neonic Corporation
- Applicants: Neonic Corporation
- Inventors: Mishig Davaadorj; Samuel H. Miller; Cormac S. Siegfried; Malcolm Gabbard
- Web page: https://patentgrep.com/patent/US20190173594A1

## CPC

- G01N 15/1404: Handling flow, e.g. hydrodynamic focusing
- G01N 15/1425: {using an analyser being characterised by its control arrangement}
- G01N 15/1433: using image recognition
- G01S 5/0205: {Details}
- G01S 5/0289: {of multiple transceivers, e.g. in ad hoc networks}
- G06N 3/088: Non-supervised learning, e.g. competitive learning
- G06N 20/00: Machine learning
- H04B 17/318: Received signal strength
- H04W 4/023: {using mutual or relative location information between multiple location based services [LBS] targets or of distance thresholds}
- H04W 4/80: Services using short range communication, e.g. near-field communication [NFC], radio-frequency identification [RFID] or low energy communication
- H04W 24/10: Scheduling measurement reports {; Arrangements for measurement reports}
- H04W 64/003: {locating network equipment}
- H04W 84/18: Self-organising networks, e.g. ad-hoc networks or sensor networks
- H04W 88/085: {Access point devices with remote components}
- G01N 15/01: specially adapted for biological cells, e.g. blood cells (investigating sedimentation of particle suspensions in blood G01N15/05)
- G01N 2015/1006: {for cytology}
- G01N 2015/142: {Acoustic or ultrasonic focussing}
- G01N 2015/1486: {Counting the particles}
- G01S 2205/02: Indoor
- H04W 48/20: Selecting an access point
- G01N: INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES (measuring or testing apparatus or processes other than immunoassay, involving enzymes or microorganisms C12M, C12Q)
- 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
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- H04B: TRANSMISSION
- H04W: WIRELESS COMMUNICATION NETWORKS (broadcast communication H04H; communication systems using wireless links for non-selective communication, e.g. wireless extensions H04M1/72)

## Abstract

Systems and methods for wireless location are disclosed. In one aspect, a method for wireless location includes collecting signal strength) values from one or more nodes (e.g., mobile devices) in an area over a time interval. The nodes receive wireless signals from one or more other transmitting nodes, where the signal strength values are representative of the signal strengths of the wireless signals. The method further includes normalizing the collected signal strength values and evaluating respective locations within the area of the nodes based on the normalized signal strength values. In a further aspect, the evaluated locations of the nodes may be used to execute an automated light show over the area, by instructing the nodes to display certain color or pattern at their locations in the area.

## Claims

### Claim 1

A method for wireless location, comprising:
collecting a plurality of signal strength values from a plurality of nodes over a time interval, wherein the signal strength values are representative of signal strengths of respective plurality of wireless signals transmitted by at least one of the nodes to at least one other of the nodes, and wherein the nodes are located in an area;
normalizing the collected signal strength values; and
evaluating respective locations within the area of the nodes based on the normalized signal strength values.

### Claim 2 (depends on 1)

The method of claim 1, further comprising orienting the location coordinates to an orientation representative of an orientation of the nodes.

### Claim 3 (depends on 1)

The method of claim 1, wherein the area includes an indoor facility.

### Claim at position 4

4-5. (canceled)

### Claim 6 (depends on 1)

The method of claim 1, wherein a distance between at least two of the nodes is less than 5 m.

### Claim 7 (depends on 1)

The method of claim 1, wherein the nodes comprise mobile devices.

### Claim 8 (depends on 1)

The method of claim 1, wherein at least one of the nodes include a different equipment for transmitting or receiving the wireless signal than at least one other of the nodes.

### Claim at position 8

9-10. (canceled)

### Claim 11 (depends on 1)

The method of claim 1, wherein the wireless signals comprise Bluetooth signals.

### Claim 12 (depends on 1)

The method of claim 1, wherein the wireless signals comprise wireless signals of communication in a mesh network, and wherein at least two of the nodes are part of the mesh network.

### Claim 13 (depends on 1)

The method of claim 1, wherein the normalizing comprises normalizing the signal strength values with a normalization function, wherein the normalization function is based on a fit of previously collected signal strength values to a pre-determined normalization range.

### Claim 14 (depends on 13)

The method of claim 13, wherein the previously collected signal strength values are collected from nodes with similar groups of wireless equipments as the nodes for transmitting or receiving the wireless signals.

### Claim 15 (depends on 13)

The method of claim 13, wherein the fit comprises an exponential fit.

### Claim 16 (depends on 13)

The method of claim 13, wherein the normalization function is based on a fit of averages of the previously collected signal strength values of nodes at a substantially same distance.

### Claim 17

(canceled)

### Claim 18 (depends on 1)

The method of claim 1, wherein the evaluating comprises evaluating using machine learning technique on the normalized signal strength values.

### Claim 19 (depends on 18)

The method of claim 18, wherein the machine learning technique comprises a self-organizing map (SOM).

### Claim at position 18

20-21. (canceled)

### Claim 22 (depends on 18)

The method of claim 18, wherein the machine learning technique is trained to minimize an error to the locations.

### Claim at position 20

23-26. (canceled)

### Claim 27 (depends on 1)

The method of claim 1, further comprising transmitting respective data to at least one of the nodes for controlling respective displays of the nodes based on the data.

### Claim 28 (depends on 27)

The method of claim 27, wherein the transmitting is synchronized with music playing in the area in substantially real-time.

### Claim 29

(canceled)

### Claim 30 (depends on 12)

The method of claim 12, wherein the evaluating is performed by the mesh network.

### Claim 31

A wireless location system, comprising:
a plurality of wireless nodes positioned in an area, wherein at least one of the nodes is transmitting one or more wireless signals in the area, wherein the wireless signals are received by at least one of the other nodes, wherein signal strengths of the respective wireless signals are detected by the at least one other nodes as respective signal strength values; and
one or more computational equipments configured for normalizing the collected signal strength values and evaluating respective locations within the area of the nodes based on the normalized signal strength values.

### Claim at position 26

32-60. (canceled)

### Claim 61

A method for wireless location, comprising:
collecting a plurality of signal strength values from a plurality of nodes over a time interval, wherein the signal strength values are representative of signal strengths of respective plurality of wireless signals transmitted by at least one of the nodes to at least one other of the nodes, and wherein the nodes are located in an area;
normalizing the collected signal strength values, wherein the normalizing comprises normalizing the signal strength values with a normalization function, wherein the normalization function is based on a fit of previously collected signal strength values to a pre-determined normalization range;
evaluating respective locations within the area of the nodes based on the normalized signal strength values, wherein the evaluating comprises evaluating using machine learning technique on the normalized signal strength values, and wherein the machine learning technique comprises a self-organizing map (SOM); and
orienting the location coordinates to an orientation representative of an orientation of the nodes,
wherein the area includes an indoor facility less than 40 m by 40 m, wherein a distance between at least two of the nodes is less than 5 m, wherein the nodes comprise mobile devices.

### Claim at position 28

62-81. (canceled)