Location data hub for mobile indoor service

Abstract

To provide a location data hub for a mobile indoor service.SOLUTION: This embodiment provides a location data hub for a mobile indoor service that uses artificial intelligence algorithms to generate reliable location information on the basis of big data from various wireless signals received from mobile devices and data that can be analyzed using AI camera functions, in the form of an SDK (Software Development Kit) that can be used on various mobile devices.SELECTED DRAWING: Figure 1B

Description

[Technical Field]

[0001] One embodiment of the present invention relates to a location data hub for mobile indoor services. [Background technology]

[0002] The following discussion merely provides background information related to the present embodiment and does not constitute prior art.

[0003] Conventionally, to recognize location information using UWB wireless signals, TDoA (Time Difference of Arrival) and TWR (Two-Way Ranging) are used for signal transmission and reception. Multiple access is supported using a time division method so that multiple devices can communicate without wireless interference.

[0004] To support multiple connections, the time division method allocates time at regular intervals. This value allows multiple connections by providing a margin of time required for signal transmission. The TWR method is preferred for calculating accurate location information, but since the TWR has a structure for sending and receiving signals, it must guarantee different intervals during time division allocation compared to the TDoA method.

[0005] Previously, there were no rules for multiple access control using UWB wireless signals. Rather than multiple access control, UWB adopted a method to avoid mutual interference by using random access.

[0006] To support multiple connections, it is being considered to adopt a time-division allocation method that supports random connections within a specific time range between an anchor and a smartphone.

[0007] However, communication methods using random connections can cause interference with information provided in two-way communication and can often be unprotected when the purpose is to transmit information rather than for positioning purposes.

[0008] Until recently, indoor positioning technology using smartphones (mobile devices) was based on LTE, Wi-Fi, and BLE signal strength (RSSI). Due to technical limitations, the accuracy of location was not satisfactory to users, and service was limited. With the incorporation of UWB communication technology into iPhones and Androids, it has become possible to calculate ToF (Time of Flight) using wireless technology rather than the existing signal strength, making it possible to calculate indoor positions with higher accuracy. Existing BLE and Wi-Fi technologies are also being upgraded with the incorporation of new standardized distance calculation wireless technology. Summary of the Invention [Problem to be solved by the invention]

[0009] The purpose of this embodiment is to provide a location data hub for mobile indoor services that can provide reliable location information based on big data using artificial intelligence algorithms from various wireless signals received from mobile devices and data that can be analyzed using AI camera functions in the form of an SDK (Software Development Kit) that can be used on various mobile devices. [Means for solving the problem]

[0010] According to one aspect of this embodiment, there is provided a location data hub including: a DB that stores mapping information in which a plurality of sensing values ​​previously collected are mapped for each divided area obtained by dividing a predetermined area using predetermined identification information; a data acquisition unit that acquires the plurality of sensing values ​​from a mobile device; a first reference position determination unit that determines a first reference position for the mobile device based on a Received Signal Strength Indicator (RSSI) of a specific sensing value among the plurality of sensing values ​​and a reference position of the same category as the specific sensing value included in the mapping information; and a second reference position determination unit that determines a second reference position for the mobile device based on remaining sensing values ​​among the plurality of sensing values ​​excluding the specific sensing value and information having the same category as the remaining sensing values ​​included in the mapping information. [Effects of the Invention]

[0011] As described above, according to this embodiment, it is possible to provide various wireless signals received from mobile devices and data that can be analyzed using AI camera functions in the form of an SDK (Software Development Kit) that can utilize reliable location information based on big data using artificial intelligence algorithms in various mobile devices. [Brief explanation of the drawings]

[0012] [Figure 1A] FIG. 2 illustrates a location data hub according to an embodiment of the present invention. [Figure 1B] FIG. 2 illustrates a location data hub according to an embodiment of the present invention. [Figure 2] 1 is a diagram illustrating a method for providing a user's location based on a VLAM (Visual Localization And Mapping) technique according to an embodiment of the present invention. [Figure 3] FIG. 4 is a diagram showing position data according to the present embodiment. [Figure 4] FIG. 10 is a diagram illustrating a process in which data is transmitted from a VPS anchor to a location data hub according to this embodiment. [Figure 5] FIG. 1 is a diagram illustrating location data using a VPS anchor, BLE, UWB, and Wi-Fi signals according to an embodiment of the present invention. [Figure 6] 10A and 10B are diagrams showing a TPH reference anchor and a TPH reference position according to the present embodiment. [Figure 7] 10A to 10C are diagrams illustrating an operation process of the location data hub according to the present embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0013] The present embodiment will be described in detail below with reference to the accompanying drawings.

[0014] 1A and 1B are diagrams illustrating a location data hub according to an embodiment of the present invention.

[0015] The location data hub 110 in this embodiment measures the user location using a VPS anchor, UWB (Ultra-Wide Band), BLE beacon (Bluetooth Low Energy Beacon), Wi-Fi RSSI (Received Signal Strength Indicator), LTE / 5G, Wi-Fi ranging, and BLE ranging.

[0016] The location data hub 110 can provide various wireless signals received from mobile devices and data that can be analyzed using AI camera functions in the form of an SDK (Software Development Kit) that can utilize reliable location information based on big data using artificial intelligence algorithms on various mobile devices.

[0017] The location data hub 110 measures a location using Wi-Fi RSSI-based fingerprinting-based analysis data. The location data hub 110 measures a location using BLE RSSI-based fingerprinting-based analysis data. The location data hub 110 measures a location using LTE / 5G base station-based RSSI-based analysis data provided by a mobile carrier. The location data hub 110 measures a location using UWB ranging-based analysis data. The location data hub 110 measures a location using BLE channel sounding-based analysis data. The location data hub 110 measures a location using Wi-Fi FTM (Fine Time Measurement)-based analysis data. The location data hub 110 measures a location using data converted from location photos on portals (Google / Naver / Kakao) and the like into VPS-based data. The location data hub 110 measures a location using beacon positioning.

[0018] The location data hub 110 determines a precise location using a combination of BLE RSSI and Wi-Fi RSSI. The location data hub 110 determines a precise location using a combination of BLE RSSI and BLE precise distance. The location data hub 110 determines a precise location using a combination of BLE RSSI and Wi-Fi precise distance.

[0019] The location data hub 110 is a beacon bank that stores beacon data in a database. For example, if the location data hub 110 is a mobile device, when it scans a beacon installed in an office, it can send relevant information (e.g., coupons) to the office using a positioning application installed in the mobile device.

[0020] The location data hub 110 stores UWB distance values, Wi-Fi distance values, and camera-based location recognition values ​​in a database. The location data hub 110 stores BLE RSSI, beacon data, UWB distance information, and Wi-Fi ranging information in a database for use. For example, the location data hub 110 can provide location-specific guidance when a user enters a specific location using the databased sensing values. The location data hub 110 performs precise location measurement using at least two of BLE, UWB, and Wi-Fi.

[0021] When the location data hub 110 stores sensing data in a database, the more categories in the database, the more accurate the positioning becomes. The location data hub 110 can store and store the BLE range for each location in addition to the RSSI of the beacon.

[0022] A mobile device can use the installed SDK to receive precise positioning services using the DB established in the location data hub 110. After connecting to the location data hub 110 using the installed SDK, the mobile device can check its own location or the location of others using the DB established in the location data hub 110.

[0023] The location data hub 110 stores distance measurement information collected through various communication methods, including Wi-Fi, BLE, UWB, LTE, and 5G, in a database, and performs precise positioning based on the databased sensing values ​​and distance measurement information.

[0024] The location data hub 110 performs highly accurate and precise positioning by combining RSSI and distance measurement information from Wi-Fi, BLE, UWB, LTE, and 5G. The location data hub 110 can perform precise positioning by using information captured using a camera on a mobile device, recognizing the image based on AI, and storing the image in a database. The location data hub 110 can perform precise positioning by objectifying signboards, sales booths, etc. in an image.

[0025] When a beacon or anchor is not identified, the location data hub 110 performs reference positioning based on multiple distance data, selects the closest candidates, and then measures the location again with some error based on the reference position.

[0026] The location data hub 110 first determines a rough location and then performs a second, precise location using UWB, anchors, and BLE beacons installed in the vicinity. The location data hub 110 performs a second location based on the anchor location of the connected device. The location data hub 110 performs indexing based on a reference location.

[0027] The location data hub 110 determines the current closest reference location, and if there is a location where sensed data values ​​are available in the corresponding mobile device and there is an empty area, it can determine one of a number of location methods when performing secondary precise positioning. If UWB data is not collected, the location data hub 110 can perform precise positioning using BLE2 and Wi-Fi2 in addition to UWB. When performing secondary precise positioning, the location data hub 110 identifies and uses multiple anchors and multiple beacons in a table in the DB.

[0028] In one step, the location data hub 110 compares databased reference locations with data received from the mobile device based on data sensing values ​​associated with various positioning methods measured using the mobile device to find the closest data reference location.

[0029] In the second step, the location data hub 110 selects a method for precise positioning based on whether or not it has received data to be used for precise positioning from the sensed values, and selects anchors or beacons installed in nearby locations to perform precise positioning. The location data hub 110 performs precise positioning using UWB, BLE, or Wi-Fi.

[0030] The location data hub 110 stores measured RSSI signals (measured RSSI values ​​at a specific reference location) for various communication methods in a database. The location data hub 110 determines the location by comparing information received from a mobile device with the database information. The location data hub 110 stores two or more RSSI signals from UWB, BLE, and Wi-Fi in a DB table for each reference location and then compares them.

[0031] The location data hub 110 performs positioning using a combination of BLE and UWB, BLE and BLE2, and BLE and Wi-Fi. The location data hub 110 primarily uses BLE RSSI. The location data hub 110 combines one or more of Wi-Fi RSSI, BLE distance, and Wi-Fi distance, combines it with BLE RSSI, and compares it with a reference table to determine a reference location.

[0032] The position data hub 110 basically performs positioning using a distance positioning method that uses BLE RSSI, and measures the reference position using at least one of various distance positioning methods.

[0033] The location data hub 110 according to this embodiment includes a DB 102, a data acquisition unit 104, a first reference location positioning unit 106, a second reference location positioning unit 108, and a mixed location positioning unit 109. The components included in the location data hub 110 are not necessarily limited to these.

[0034] The components included in the location data hub 110 may be connected to a communication path that connects software modules or hardware modules within the device and operate organically with each other. These components communicate using one or more communication buses or signal lines.

[0035] Each component of the location data hub 110 shown in FIG. 1B represents a unit that processes at least one function or operation, and may be realized as a software module, a hardware module, or a combination of software and hardware.

[0036] DB102 refers to a general data structure implemented in the storage space (hard disk or memory) of a computer system using a database management program (DBMS). DB102 refers to a data storage format that allows data search (extraction), deletion, editing, addition, etc. DB102 has fields or elements to achieve its own functions.

[0037] The DB 102 stores mapping information in which a plurality of sensing values ​​collected in advance are mapped to each of the divided areas obtained by dividing a predetermined area using predetermined identification information.

[0038] The data acquisition unit 104 acquires a plurality of sensing values ​​from the mobile device.

[0039] The multiple sensing values ​​include at least two of Wi-Fi RSSI, BLE RSSI, UWB RSSI, LTE / 5G RSSI, UWB Ranging, BLE Channel Sounding, Wi-Fi FTM (Fine Time Measurement), and Vision.

[0040] The first reference position positioning unit 106 locates a first reference position for the mobile device based on a received signal strength indicator (RSSI) of a specific sensing value among a plurality of sensing values ​​and a reference position of the same category as the specific sensing value included in the mapping information.

[0041] The first reference position determination unit 106 extracts a plurality of BLE RSSIs included in a plurality of sensing values ​​and selects a BLE RSSI having the greatest strength from the plurality of BLE RSSIs. The first reference position determination unit 106 extracts identification information having the same category as the BLE having the greatest strength from the mapping information. The first reference position determination unit 106 determines a first reference position of the mobile device based on the reference position mapped to the identification information having the same category as the BLE having the greatest strength.

[0042] The second reference position positioning unit 108 positions a second reference position for the mobile device based on the remaining sensing values ​​excluding a specific sensing value among the plurality of sensing values ​​and information having the same category as the remaining sensing values ​​included in the mapping information.

[0043] The second reference position determination unit 108 extracts remaining sensing values ​​excluding BLE from the plurality of sensing values, and determines a second reference position for the mobile device based on a reference position having the same category as the remaining sensing values ​​included in the mapping information.

[0044] The second reference position determination unit 108 extracts multiple Wi-Fi RSSIs included in the multiple sensing values ​​and selects the Wi-Fi RSSI with the greatest strength from the multiple Wi-Fi RSSIs. The second reference position determination unit 108 extracts identification information having the same category as the Wi-Fi with the greatest strength from the mapping information. The second reference position determination unit 108 determines a second reference position for the mobile device based on the reference position mapped to the identification information having the same category as the Wi-Fi with the greatest strength.

[0045] The second reference position determination unit 108 extracts a plurality of BLE rangings included in the plurality of sensing values ​​and selects a BLE ranging having a closest distance based on the plurality of BLE rangings. The second reference position determination unit 108 extracts identification information having the same category as the BLE having the closest distance from the mapping information. The second reference position determination unit 108 determines a second reference position for the mobile device based on the reference position mapped to the identification information having the same category as the BLE having the closest distance.

[0046] The second reference position determination unit 108 extracts a plurality of Wi-Fi rangings included in the plurality of sensing values, and selects a Wi-Fi ranging having the closest distance based on the plurality of Wi-Fi rangings. The second reference position determination unit 108 extracts identification information having the same category as the Wi-Fi having the closest distance from the mapping information. The second reference position determination unit 108 determines a second reference position for the mobile device based on the reference position mapped to the identification information having the same category as the Wi-Fi having the closest distance.

[0047] The mixed positioning unit 109 determines a precise position of the mobile device based on the first reference position and the second reference position. The mixed positioning unit 109 determines one of an average value, a weighted average value, an interpolated value, and an intermediate value of the first reference position and the second reference position as the precise position of the mobile device.

[0048] FIG. 2 is a diagram illustrating a method for providing a user's location based on a VLAM (Visual Localization And Mapping) technique according to this embodiment.

[0049] 3D scanning of the target area is performed (S210). In step S210, a person wears the 3D mapping equipment and performs 3D scanning of the target area. The real view is modeled in 3D and sensor data is collected to generate a Visual-Geo DB for camera-based precise positioning.

[0050] A DB is constructed by 3D modeling (S220). In step S220, a 3D mesh is generated and a Visual-Geo DB is created.

[0051] The user takes a picture of his / her location using a mobile device (S230). In step S230, the user takes a picture of the surroundings with a camera and then transmits the picture to a cloud server.

[0052] The cloud server calculates the location (S240). In step S240, the cloud server applies deep learning-based SLAM (Simultaneous Localization and Mapping) technology to measure its own location and simultaneously create a map of the surrounding environment. The cloud server estimates the exact camera position / direction by matching the image taken by the customer's mobile device with the Visual-Geo DB.

[0053] The user's current location information is provided (S250).

[0054] 2, steps S210 to S250 are executed sequentially, but this is not necessarily limited to this. In other words, the steps shown in FIG. 2 may be executed in a modified manner, or one or more steps may be executed in parallel, and FIG. 2 is not limited to a chronological order.

[0055] As described above, the method for providing a user location based on the VLAM technology according to this embodiment, as shown in Fig. 2, may be implemented as a program and recorded on a computer-readable recording medium. The computer-readable recording medium on which the program for implementing the method for providing a user location based on the VLAM technology according to this embodiment is recorded includes any type of recording device on which data readable by a computer system is stored.

[0056] FIG. 3 is a diagram showing position data according to this embodiment.

[0057] The location data hub 110 measures and manages the reference position of the BLE beacon and the BLE RSSI based calculated position based on the reference position.

[0058] The location data hub 110 measures and manages the reference position of the Wi-Fi AP and the Wi-Fi RSSI-based calculated position from the reference position.

[0059] The location data hub 110 measures and then manages the reference position of the LTE / 5G base station and the signal strength based calculated position (LTE / 5G RSSI based calculated position) from the reference position.

[0060] The location data hub 110 measures and then manages the reference position of the UWB anchor and the UWB ranging-based calculated position from the reference position.

[0061] The location data hub 110 measures and manages the reference position of a BLE beacon (Channel Sounding) and the BLE Channel Sounding based calculated position from the reference position. The location data hub 110 measures and manages the reference position of a Wi-Fi AP (FTM) and the UWB ranging calculated position from the reference position.

[0062] The location data hub 110 can anchor images acquired from store interior images acquired using Google / Apple Map Portal APIs based on ML (Machine Learning). The location data hub 110 measures and manages the reference position of the ML (Machine Learning)-based image anchoring. The location data hub 110 recognizes image anchors using mobile camera Vision AI technology, measures and manages the user's own location. The location data hub 110 recognizes the user's indoor / outdoor location using BLE / Wi-Fi / UWB / Vision information in the form of a mobile SDK and provides information.

[0063] The location data hub 110 can generally provide various location-based services indoors by calculating indoor locations more accurately than fingerprinting methods based on wireless signal strength. The location data hub 110 will improve its accuracy in various indoor spaces in the future as standardized UWB / BLE / Wi-Fi technologies become more widespread. The location data hub 110 enables centimeter (CM)-level accurate positioning indoors for mobile devices equipped with UWB functionality. The location data hub 110 can be used for emergency rescue and disaster relief.

[0064] The location data hub 110 manages BLE RSSI, Wi-Fi RSSI, and LTE & 5G RSSI reference information. The location data hub 110 manages UWB ranging, BLE channel sounding, and Wi-Fi FTM information. The location data hub 110 manages anchors that can be recognized by image recognition in Google Maps Place images. The location data hub 110 calculates the user's location using image anchor recognition from a mobile camera. The location data hub 110 provides location information to the user using TPH information in the mobile SDK.

[0065] FIG. 4 is a diagram illustrating a process in which data is transmitted from a VPS anchor to a location data hub according to this embodiment.

[0066] A VPS (Virtual Private Server) anchor performs image crawling based on a path on a mobile device to generate image crawling data. The VPS anchor extracts only place images from the image crawling data using AI analysis. The VPS anchor transmits location measurement information based on the place images to the location data hub 110.

[0067] FIG. 5 is a diagram illustrating location data using a VPS anchor, BLE, UWB, and Wi-Fi signals according to this embodiment.

[0068] The location data hub 110 determines a precise location using a combination of BLE RSSI and Wi-Fi RSSI, and then maps the precise location on a map using VLAM.The location data hub 110 determines a precise location using a combination of VPS anchor, BLE, UWB, and Wi-Fi radio wave round trip time (RTT), and then maps the precise location on a map using VLAM.

[0069] FIG. 6 is a diagram showing the TPH reference anchor and the TPH reference position according to this embodiment.

[0070] The location data hub 110 classifies the categories of TPH reference anchors into BLE, Wi-Fi, UWB, BLE2, Wi-Fi2, LTE, 5G, and Vision, maps them in a table, and stores them. The location data hub 110 classifies the types of TPH reference anchor categories into Beacon, AP, Anchor, Carrier, and Image, and maps them in a table and stores them. The location data hub 110 maps and stores installation location coordinates (X, Y, Z) for the categories of TPH reference anchors in a table. The location data hub 110 maps and stores installation addresses for the categories of TPH reference anchors in a table.

[0071] The location data hub 110 classifies the type of the TPH reference location into point and area and stores the type in a table. The location data hub 110 maps and stores the installation position coordinates (X, Y, Z) of the TPH reference location in a table. The location data hub 110 maps and stores the signal strength (RSSI) of BLE, Wi-Fi, LTE, 5G, UWB, BLE2, Wi-Fi2, and Vision for the TPH reference location in a table.

[0072] The TPH reference location stored in the table stores values ​​for each communication method based on the current location of the terminal. The location data hub 110 determines the reference location by extracting from the database the signal most similar to the signal received from the mobile device. The location data hub 110 then searches for the closest location among the multiple reference locations.

[0073] FIG. 7 is a diagram illustrating an operation process of the location data hub according to this embodiment.

[0074] The location data hub 110 downloads reference anchor information using the SDK and then starts indoor positioning (S710). The location data hub 110 performs BLE / Wi-Fi RSSI-based indoor positioning (S720).

[0075] The location data hub 110 selects the closest reference device (Ref Device) from the mobile device based on the indoor location result (S730). The location data hub 110 checks whether a ranging device (Ranging Device) has been selected (S740).

[0076] If the device is selected as a ranging device as a result of the confirmation in step S740, the location data hub 110 calculates the ranging distance value and calculates the final location (S750).The location data hub 110 provides the location information to the user using the SDK (S760).

[0077] The location data hub 110 restarts the BLE / Wi-Fi RSSI location calculation (S770). If the device is not selected as a ranging device as a result of the check in step S740, the location data hub 110 selects another ranging device via the server (S780).

[0078] 7, steps S710 to S780 are executed sequentially, but this is not necessarily limited to this. In other words, the steps shown in FIG. 7 may be executed in a modified manner, or one or more steps may be executed in parallel, and FIG. 7 is not limited to a chronological order.

[0079] As described above, the operation process of the position data hub according to this embodiment shown in Figure 7 may be implemented by a program and recorded on a computer-readable recording medium. The computer-readable recording medium on which the program for implementing the operation process of the position data hub according to this embodiment is recorded includes any type of recording device on which data readable by a computer system is stored.

[0080] The above description merely exemplifies the technical concept of the present embodiment, and various modifications and variations may be made by a person skilled in the art without departing from the essential characteristics of the present embodiment. Therefore, the present embodiment is intended to illustrate, not limit, the technical concept of the present embodiment, and the scope of the technical concept of the present embodiment is not limited by such an embodiment. The scope of protection of the present embodiment should be interpreted by the appended claims, and all technical concepts within the scope equivalent thereto should be interpreted as being included in the scope of the present embodiment. [Explanation of symbols]

[0081] 110 Location Data Hub 102 DB 104 Data Acquisition Unit 106 First reference positioning unit 108 Second reference positioning unit 109 Mixed Positioning Unit

Claims

1. a DB for storing mapping information in which a plurality of sensing values ​​previously collected are mapped for each divided area obtained by dividing a predetermined area using predetermined identification information; a data acquisition unit that acquires a plurality of sensing values ​​from a mobile device; a first reference position determination unit that determines a first reference position for the mobile device based on a received signal strength indicator (RSSI) of a specific sensing value among the plurality of sensing values ​​and a reference position of the same category as the specific sensing value included in the mapping information; a second reference position determining unit that determines a second reference position for the mobile device based on remaining sensing values ​​excluding the specific sensing value among the plurality of sensing values ​​and information having the same category as the remaining sensing values ​​included in the mapping information; 1. A location data hub comprising:

2. The plurality of sensing values ​​are 2. The location data hub of claim 1, comprising at least two of Wi-Fi RSSI, BLE RSSI, UWB RSSI, LTE / 5G RSSI, UWB Ranging, BLE Channel Sounding, Wi-Fi FTM (Fine Time Measurement), and Vision.

3. a hybrid position determination unit for determining a precise position of the mobile device based on the first reference position and the second reference position; The location data hub of claim 1 further comprising:

4. The first reference position positioning unit extracting a plurality of the BLE RSSIs included in the plurality of sensing values, and selecting a BLE RSSI having the greatest strength from the plurality of the BLE RSSIs; extracting, from the mapping information, identification information having the same category as the BLE having the greatest strength; 3. The location data hub of claim 2, wherein a first reference location for the mobile device is determined based on a reference location mapped to an identification having the same category as the BLE having the greatest strength.

5. The second reference position measurement unit extracting remaining sensing values ​​excluding the BLE from the plurality of sensing values; The location data hub of claim 3 , further comprising: determining a second reference location for the mobile device based on a reference location having the same category as the remaining sensing values ​​included in the mapping information.

6. The second reference position measurement unit extracting a plurality of the Wi-Fi RSSIs included in the plurality of sensing values, and selecting a Wi-Fi RSSI having the greatest strength from the plurality of the Wi-Fi RSSIs; extracting identification information having the same category as the Wi-Fi with the greatest strength from the mapping information; 4. The location data hub of claim 3, further comprising: determining a second reference location for the mobile device based on a reference location mapped to an identification having the same category as the Wi-Fi with the greatest strength.

7. The second reference position measurement unit extracting a plurality of the BLE rangings included in the plurality of sensing values, and selecting a BLE ranging having a closest distance based on the plurality of the BLE rangings; extracting, from the mapping information, identification information having the same category as the BLE having the closest distance; 4. The location data hub of claim 3, further comprising: determining a second reference location for the mobile device based on a reference location mapped to an identity having the same category as the BLE having the closest distance.

8. The second reference position measurement unit extracting a plurality of the Wi-Fi rangings included in the plurality of sensing values, and selecting a Wi-Fi ranging having a closest distance based on the plurality of the Wi-Fi rangings; extracting, from the mapping information, identification information having the same category as the Wi-Fi having the closest distance; 4. The location data hub of claim 3, further comprising: determining a second reference location for the mobile device based on a reference location mapped to identification information having the same category as the Wi-Fi with the closest distance.

9. The mixed position measurement unit The location data hub of claim 1 , wherein the precise location of the mobile device is determined as one of an average value, a weighted average value, an interpolated value, and an intermediate value of the first reference location and the second reference location.

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