KR20150089633A - APPARATUS FOR INDOOR POSITIONING BASED ON WiFi FINGERPRINTING AND METHOD THEREOF - Google Patents

Abstract

The present invention relates to an apparatus for indoor positioning based on Wi-Fi fingerprinting and a method thereof. The apparatus of the present invention comprises: an indoor positioning module for receiving Wi-Fi signals at the current position in real time and determining the raw position (RawPos) by referring to the data in a pre-created database (DB); a stabilizing module for receiving and accumulating an input of the determined RawPos, calculating the average coordinate of the coordinates of the RawPos of a preset number, determining the stable position (StablePos), determining that a failure position has occurred when the distance from a preset standard position to the RawPos exceeds a preset distance, calculating the average coordinate for the RawPos coordinate of the preset number coming under a failure position, and determining the StablePos; and a final position determining module for determining the StablePos calculated when the failure position does not occur or the StablePos calculated when the failure position occurs continuously as the final position.

Description

FIELD OF THE INVENTION [0001] The present invention relates to a WiFi fingerprinting indoor positioning device and method,

The present invention relates to a Wi-Fi fingerprint-based indoor positioning apparatus and method, and more particularly, to a Wi-Fi fingerprint-based indoor positioning apparatus and method for rapidly selecting DB (Data Base) information based on a Wi-Fi fingerprint to reduce a calculation amount for positioning, And more particularly to a WiFi fingerprint-based indoor positioning apparatus and method for improving the stability and accuracy of a positioning result in the case of performing a positioning operation.

Recently, GPS (Global Positioning System) has been installed in most smartphones, and location-based services have begun to be used in various areas of real life. In particular, as GPS has been freely available in various apps (APP) of smart phones, location information and various services have been combined.

In recent years, a location-based search service that provides recommendation of nearby restaurants based on my current location information, providing real estate information, gas station price information, etc., along with a navigation service (navigation service) Is being actively used.

However, signals generated by GPS satellites are very weak as they pass through more than 20,000 km of air, making it impossible to penetrate the interior of the building. Therefore, it is impossible to use location-based services such as navigation service in the room. This is because most location-based services rely on GPS.

Recently, indoor positioning technology using WiFi signal has been developed as a technology for replacing GPS, and the technology for improving indoor map accuracy and low cost has been developed, Is gradually becoming a reality.

For reference, the indoor positioning technique using the initial WiFi signal estimates the distance to each WiFi access point (AP) based on how much the received WiFi signal is initially attenuated, and estimates the distance of each WiFi access point And calculating the position of the user using the position and the calculated distance. However, this method has a problem in that the WiFi signal is not reduced uniformly according to the characteristics of the indoor space, and is irregularly reduced due to a collision with a wall or various objects.

In order to solve the above problems, a fingerprint method has been developed.

The fingerprint method divides the indoor space into virtual lattices, measures the intensity of each WiFi signal for each lattice, forms a database in the form of a fingerprint, and then compares the strength of the WiFi signal measured by the WiFi signal with the database So that the accuracy can be improved more than the conventional method.

However, in the indoor positioning method using the conventional fingerprint method, a WiFi signal received in real time in a fingerprint DB (Data Base) and a method of selecting a point with the smallest error as a positioning result are applied. In this case, in order to determine the current position, it is necessary to compare with all the points of the DB, which increases the calculation amount and the positioning calculation time. In addition, fingerprint - based positioning based on WiFi signal strength is problematic in that the positioning result is unstable and accuracy is poor due to a significant change in signal strength due to environmental changes.

BACKGROUND ART [0002] The background art of the present invention is disclosed in Korean Patent Registration No. 10-1084478 (registered on Nov. 10, 2011, a method and system for correcting network-based location information in a shaded area using a continuous positioning function of a GPS mobile terminal).

SUMMARY OF THE INVENTION The present invention has been made in order to solve the above problems, and it is an object of the present invention to provide a WiFi fingerprint-based indoor positioning device and method capable of rapidly selecting DB (Data Base) information based on Wi- The purpose is to provide.

Another object of the present invention is to provide a WiFi fingerprint-based indoor positioning apparatus and method which can improve the stability and accuracy of a positioning result when only a Wi-Fi signal is used without using other positioning sensors.

A WiFi fingerprint-based indoor positioning apparatus according to an aspect of the present invention includes a WiFi fingerprint-based indoor positioning device for receiving WiFi signals at a current location in real time and for determining an initial position (RawPos) by referring to data of a DB Positioning module; (RawPos) as input, determines a stable position (StablePos) by calculating an average coordinate of a predetermined number of initial positions (RawPos) coordinates, and determines the initial position (RawPos) ) Is determined to be a failure position, and determines the stable position (StablePos) by calculating an average coordinate of a predetermined number of initial positions (RawPos) coordinates corresponding to the failure position Stabilization module; And a final positioning module that determines a stable position (StablePos) calculated when the failure position does not occur, or a stable position (StablePos) calculated when the failure position occurs consecutively as a final position. .

In the present invention, the DB (Data Base) is a hash map DB (HashMapDB).

In the present invention, the hash map DB (Data Base) collects and records n APs actually received at each point in the positioning target area and a signal strength (RSSI: Received Signal Strength Indicator) of the corresponding AP, ), Generates a DB (RawDB) using all of the collected scan data (ScanData), selects an AP having the strongest signal strength (RSSI) at each point of the DB (RawDB) (ScanData) is set to a hash map (HashMap) value (value).

In the present invention, the indoor positioning module selects APs within a predetermined signal range based on the signal strength of an AP (Access Point) having the strongest signal strength among the WiFi signals received at the current location And selects a comparison object in the DB using the selected key at the current point, and calculates the signal intensity error (Error) of the AP for each of the WiFi scan data (ScanData) And a point at which the signal intensity error is the smallest is determined as an initial position (RawPos).

In the present invention, the predetermined signal range is within Max RSSI-10 in the Max RSSI (Received Signal Strength Indicator).

In the present invention, the error of the signal intensity is calculated by the following equation (1).

(1)

는 DB의 신호세기이고,

는 스캔데이터의 신호세기.
here

Is the signal strength of the DB,

Is the signal strength of the scan data.

A WiFi fingerprint-based indoor positioning method according to an aspect of the present invention is characterized in that the indoor positioning module receives WiFi signals at a current location in real time and refers to data in a DB (Data Base) Determining; The stabilization module calculating an average point for a predetermined number of accumulated initial positions (RawPos) to determine a stable position (StablePos); And determining a stable position (StablePos) calculated as a final position by reflecting whether a failure position is continuously generated in the step of determining the stable position by the final positioning module.

In the present invention, the step of determining the initial position may include a step of the indoor positioning module collecting the scan data (ScanData) by performing a Wi-Fi scan at the current location; Selecting APs within a predetermined range based on an AP (Access Point) having the strongest signal strength (RSSI) in the scan data (ScanData); Selecting data of candidate points from a previously generated DB (HashMapDB) using APs selected by the Key; Calculating an error between the AP signal of the candidate points selected in the DB (HashMapDB) and the scanned AP signal; And selecting a point at which the calculated error is the smallest among the data of the candidate points to be the initial position (RawPos).

In the present invention, the candidate point is a point including data that is likely to be selected as a current position, and is a point selected at the current position and a point at which the same key is set in the DB.

In the present invention, the step of determining the stable position (StablePos) includes the steps of: the positioning module receiving positioning data for a current position from the indoor positioning module; Calculating a distance from a current position calculated based on the positioning data to a predetermined reference position; And accumulating the current position in the temporary positional array when the calculated distance is less than a predetermined distance and calculating an average point of points accumulated in the predetermined number of temporary positional arrangements and setting it as the stable position (StablePos) .

In the present invention, the step of determining the stable position (StablePos) may include accumulating the current position in the failure position array when the calculated distance exceeds a predetermined distance, (StablePos) by calculating an average point of the points that have not been detected.

In the present invention, regarding the reference position, if the current position is the first positioning data, the current position is the reference position, and if the previously measured positioning data exists, the positioning data is used And the calculated final position or stable position becomes the reference position.

According to the present invention, it is possible to quickly select DB (Data Base) information based on a Wi-Fi fingerprint to reduce the amount of calculation for positioning, and also to provide a positioning result when using only a Wi-Fi signal without using other positioning sensors So as to improve the stability and accuracy.

BRIEF DESCRIPTION OF THE DRAWINGS FIG. 1 is an exemplary diagram for explaining a database creation method for indoor positioning based on Wi-Fi fingerprint according to an embodiment of the present invention; FIG.
BACKGROUND OF THE INVENTION 1. Field of the Invention [0001] The present invention relates to an indoor positioning device based on a WiFi fingerprint.
Fig. 3 is an exemplary diagram for explaining the operation of the indoor positioning module in Fig. 2; Fig.
4 is a flowchart illustrating a Wi-Fi fingerprint-based indoor positioning method according to an embodiment of the present invention.
5 is a flowchart for explaining the operation of the stabilization module in more detail in FIG.
FIG. 6 is a diagram illustrating an example of comparison between positioning results obtained when a stabilization module according to an embodiment of the present invention is applied and when indoor positioning is performed without applying a stabilization module. FIG.

Hereinafter, an embodiment of a WiFi fingerprint-based indoor positioning apparatus and method according to the present invention will be described with reference to the accompanying drawings.

In this process, the thicknesses of the lines and the sizes of the components shown in the drawings may be exaggerated for clarity and convenience of explanation. In addition, the terms described below are defined in consideration of the functions of the present invention, which may vary depending on the intention or custom of the user, the operator. Therefore, definitions of these terms should be made based on the contents throughout this specification.

The WiFi fingerprint-based indoor positioning according to an embodiment of the present invention includes a DB (Data Base) generation step (Off-line phase) and a positioning phase (Real-time phase).

The positioning step is performed in real time in a WiFi fingerprint-based indoor positioning device according to an embodiment of the present invention.

The DB creation step is performed by a map provider (or a service provider).

In one embodiment of the present invention, it is assumed that a DB (e.g., RawDB, HashMapDB) for the WiFi fingerprint-based indoor positioning is already generated.

Here, the RawDB (hereafter referred to as a first DB) 110 is a DB that collects WiFi AP signal strengths collected at respective locations for fingerprint-based indoor positioning, and the HashMapDB DB 120 is a DB generated by setting an AP ID (e.g., AP1, AP2, AP3, etc.) having the strongest signal strength of each point as a key value.

Hereinafter, the steps of generating the first DB (e.g., RawDB) 110 and the second DB (e.g., HashMap DB) 120 will be described.

In the DB generation step for the WiFi fingerprint-based indoor positioning, n APs (e.g., AP1, AP2, AP3, and AP3) actually received at a positioning target position (or each point) AP4, AP5, AP6, and the like) and the signal strength (RSSI: Received Signal Strength Indicator) of the corresponding AP to generate scan data (ScanData) (E.g., RawDB) 110 is generated.

After selecting an AP having the strongest signal strength (RSSI) at each point (e.g., A point and B point) of the first DB (e.g., RawDB), the AP is set to Key, The second DB (e.g., HashMap DB) 120 is generated by setting the data (ScanData) to a value of a hash map (HashMap).

For the sake of convenience, "points" and "positions" described in the embodiments of the present invention are described as "points" in the case where positions already stored in the DB are shown for convenience of explanation, Quot; position "in the case of indicating the position to be positioned in real time using the indoor positioning apparatus according to the present invention. However, in some cases, they may be written in a mixed manner without following the above-mentioned classification.

Hereinafter, an indoor positioning apparatus for performing indoor positioning based on the DB (e.g., RawDB, HashMapDB) generated as described above will be described.

2 is a diagram illustrating a schematic configuration of a Wi-Fi fingerprint-based indoor positioning device according to an embodiment of the present invention.

2, the Wi-Fi fingerprint-based indoor positioning apparatus according to an exemplary embodiment of the present invention includes a first DB 110, a second DB 120, an indoor positioning module 130, a stabilization module 140 And a final positioning module 150.

As described above, the first DB 110 and the second DB 120 have already been created, and the DB information is provided to the indoor positioning device through at least one of the offline method and the online method I suppose.

Here, the online method refers to a method of accessing and using DB information in real time by accessing a server (not shown) providing corresponding DB information in a wire / wireless manner and using the DB information in real time. (Not shown) of the indoor positioning apparatus according to an embodiment of the present invention.

The indoor positioning module 130 selects APs within a predetermined signal range based on the signal strength of the AP having the strongest signal strength among the WiFi signals received at the current location in real time.

For example, referring to FIG. 3, APs (e.g., AP2) in a predetermined signal range (e.g., Max RSSI to Max RSSI-10) Can be selected.

In this case, the predetermined signal range can be arbitrarily set. When the signal range is reduced, the comparison object is reduced and the computation amount is small, but the positioning result can be relatively less accurate. When the signal range is widened, The positioning results can be relatively more accurate.

Next, the indoor positioning module 130 selects a comparison object from the second DB (e.g., HashMap DB) 120 using the determined key (e.g., AP1 and AP2) at the current point. That is, a point (for example, a point A, a point B, and a point C) having the same key as the key selected at the current point is selected as a comparison object.

Then, the signal intensity error of the AP for each of the comparison object found in the second DB (e.g., HashMap DB) 120 and the WiFi scan data (ScanData) of the current point is calculated, Is determined as the current position.

)는 아래의 수학식 1에 의해 계산할 수 있다.The error of the signal strength (

) Can be calculated by the following equation (1).

Where p is the signal strength of the DB and q is the signal strength of the scan data.

At this time, in the embodiment of the present invention, the determined current position is set as the initial position (RawPos).

That is, in the embodiment of the present invention, the determined current position is not determined immediately as the final position but is set to the initial position (RawPos), and the stabilization module 140 and the final positioning module 150 . ≪ / RTI >

The stabilization module 140 receives the determined initial position RawPos as an input and adds the predetermined number (e.g., five) of initial positions (RawPos) (i.e., coordinates) . At this time, even when the number of initial positions determined through the indoor positioning module 130 is five or less, the average point (i.e., coordinate) can be calculated.

Then, the stabilization module 140 determines the average position (i.e., coordinate) of the calculated initial positions RawPos as the stable position StablePos.

For example, assuming that the position (initial position) determined through the indoor positioning module 130 at the A point is P1 and the position (initial position) determined by the user through the indoor positioning module 130 at the B point is P2 , The stabilization module 140 calculates an average point of the determined initial positions P1 and P2 and determines the stable position StablePos as (P1 + P2) / 2. When the user moves again (or a predetermined time elapses) and a new initial position (RawPos) P3 is input, the average point of the three initial positions is calculated ((P1 + P2 + P3) / 3) Determine the location (StablePos). (For example, when P4, P5, and P6 are input), the stabilization module 140 determines whether or not the final input (i.e., (P6 + P4 + P5 + P6) / 5) to determine the stable position (StablePos) based on the initial position (e.g., P6)

At this time, the above procedure describes a case where the distance between the initial position (RawPos) and the stable position (StablePos) is within a certain distance. For example, when the user moves on foot, the present position is suitable for the case where the change of the current position is within the movement distance by the walking, ).

Therefore, in order to prevent an incorrect position from being determined due to a change in environment or the like, the indoor positioning module 130 may set the distance to the newly determined initial position (RawPos) based on the previously determined stable position (StablePos) If the distance (eg 50 m) is exceeded, the corresponding initial position (RawPos) is excluded in the calculation of the stable position (StablePos).

And stores the excluded initial position (RawPos) in the failure location array.

The reason is that the position calculated by the stable position (StablePos) is wrong, and the newly determined initial position (RawPos) may be more accurate. In this case, the newly determined initial position (RawPos) Because.

If an initial position (RawPos) excluded from the stable position calculation is accumulated in a predetermined number (eg, two to five) in succession, exception processing is performed.

For example, when five or more failure positions occur consecutively, an average point of the positions is calculated and determined as a stable position (StablePos).

The final positioning module 150 may calculate the stable position StablePos calculated when the failure position does not occur or the stable position StablePos calculated when the failure position occurs, , Final position).

Although the indoor positioning module 130, the stabilization module 140, and the final positioning module 150 perform the respective operations as described above, the indoor modules 130, 140 And 150 may be integrated.

4 is a flowchart illustrating a Wi-Fi fingerprint-based indoor positioning method according to an embodiment of the present invention.

As shown in FIG. 4, the indoor positioning module 130 performs WiFi scanning at the current location to collect scan data (ScanData) (S101).

Here, the scan data (ScanData) refers to signal strength information for a WiFi AP received by an indoor positioning device (e.g., a smart phone, a mobile terminal, a navigation device, or the like) at an arbitrary point (or position).

The indoor positioning module 130 selects APs within a certain range (e.g., Max RSSI to Max RSSI-10) based on the AP having the strongest signal strength of the scan data ScanData (S102 ).

The indoor positioning module 130 then transmits the data of the candidate points in the second DB 120 (e.g., HashMapDB) using the APs selected by the Key (i.e., the candidate position data (S103).

Here, the candidate data means signal strength data of a candidate point obtained by using a specific key in the second DB 120 (e.g., HashMapDB).

The indoor positioning module 130 calculates an error between the data (AP signal) of the candidate points selected in the second DB 120 (e.g., HashMapDB) and the scan data (AP signal) (S104).

Then, a point at which the calculated error is the smallest among the data of the candidate points is selected and determined as the initial position (RawPos) (S105).

The indoor positioning module 130 refers to a module that uses the second DB 120 (e.g., HashMapDB) and the scan data (ScanData) that is WiFi scanned at the current location. The initial position (e.g., RawPos) is determined by the indoor positioning module 130 in such a manner that the first DB 110 (e.g., RawDB) and the WiFi signal (i.e., WiFi ScanData) Quot; means an initial position determined by comparison.

When the initial position (RawPos) for the current position is determined as described above, the stabilization module 140 adds the predetermined number (e.g., 5) of initial positions (RawPos) (i.e., coordinates) . The average position of the calculated initial positions (RawPos) is determined as the stable position (StablePos) (S106).

Here, the stable position (e.g., StablePos) refers to a final determined position by calculating an average point of initial positions (e.g., RawPos) in the stabilization module 140.

The operation of the stabilization module 140 will be described in more detail with reference to FIG.

The final positioning module 150 determines whether or not the distance to the newly determined initial position RawPos based on the previously determined stable position StablePos is equal to or greater than a predetermined distance (e.g., 50 m) Position) is determined as the last positioning point (i.e., the final position) (S107).

5 is a flowchart illustrating the operation of the stabilization module in more detail in FIG.

5, the stabilization module 140 receives a positioning result (or positioning data, scan data) for the current position from the indoor positioning module 130 (S201).

Then, a distance from the current position calculated based on the positioning data to a predetermined reference position is calculated (S202).

Here, regarding the reference position, if the positioning data for the current position is the first positioning data, the current position becomes the reference position (or the reference position), and if there is the positioning data that was previously measured, The calculated final position (or stable position) becomes the reference point (or reference position).

If the calculated distance (i.e., the distance from the current position to the reference position) is less than a predetermined distance (e.g., 50 m), the current position is accumulated in the temporary position (or initial position) arrangement (S203).

(I.e., initial positions RawPos) in a predetermined number (e.g., five) of the temporary position arrays (i.e., arbitrary buffers for initial position storage) (S204).

Then, the calculated average point is set as a stable position (StablePos) (S205).

However, if the calculated distance (that is, the distance from the current position to the reference position) exceeds a preset distance (for example, 50 m), the current position is accumulated in the failure position arrangement (S206).

(I.e., coordinates) of consecutively accumulated points (i.e., initial positions (RawPos) determined as failure positions) in the predetermined number (e.g., five) of the failure position arrays (S207).

Then, the calculated average point is set as the stable position (StablePos) (S208).

At this time, the stable position (StablePos) set in the step S205 or S208 is set as a reference position for the current position positioning in the next step (S209).

In addition, the final positioning module 150 determines the stable position (StablePos) set in step S205 or S208 as the final position.

FIG. 6 is a diagram illustrating an example of comparison between positioning results obtained when a stabilization module according to an embodiment of the present invention is applied and when indoor positioning is performed without applying a stabilization module.

As shown in FIG. 6, the indoor positioning result using the stabilization module 140 according to an embodiment of the present invention is much closer to the ground truth information (GroundTruth) when compared with the indoor positioning result not using the stabilization module Able to know. That is, when the stabilization module 140 is applied, it can be seen that more accurate indoor positioning is performed.

As described above, according to the WiFi fingerprint-based indoor positioning, the second DB (e.g., HashMapDB) is used and the key is selected based on the signal strength. .

In addition, stabilization module (or stabilization algorithm) is applied to improve the stability of the signal strength to obtain more accurate positioning results. In addition, additional sensors other than WiFi signals (eg, accelerometer, gyro, geomagnetism, NFC etc.) So that complicated calculation process is unnecessary, and even if an inaccurate value or malfunction of the sensor occurs due to environmental change, it is not affected by such a change, so that stability can be improved.

While the present invention has been particularly shown and described with reference to exemplary embodiments thereof, it is to be understood that the invention is not limited to the disclosed embodiments, but, on the contrary, I will understand the point. Accordingly, the technical scope of the present invention should be defined by the following claims.

110: first DB 120: second DB
130: Indoor positioning module 140: Stabilization module
150: Final positioning module

Claims (12)

An indoor positioning module that receives WiFi signals in real time in real time and determines an initial position (RawPos) by referring to data of a DB (Data Base) generated in advance;
(RawPos) as input, determines a stable position (StablePos) by calculating an average coordinate of a predetermined number of initial positions (RawPos) coordinates, and determines the initial position (RawPos) ) Is determined to be a failure position, and determines the stable position (StablePos) by calculating an average coordinate of a predetermined number of initial positions (RawPos) coordinates corresponding to the failure position Stabilization module; And
(StablePos) calculated when the failure position does not occur, or a stable position (StablePos) calculated when the failure position continuously occurs, as the final position. WiFi fingerprint based indoor positioning device.
The method of claim 1, wherein the DB (Data Base)
And a hash map DB (HashMapDB).
3. The method of claim 2, wherein the hash map DB (Data Base)
The n APs actually received at each point in the positioning target area and the signal strength (RSSI: Received Signal Strength Indicator) of the corresponding AP are collected and recorded to generate scan data (ScanData)
(RawDB) using all of the collected and recorded scan data (ScanData), and selects an AP having the strongest signal strength (RSSI) at each point of the RBDB (RawDB) and sets it to a key And generates scan data (ScanData) of the corresponding point by setting a hash map (HashMap) value (value).
The indoor positioning system according to claim 1,
APs within predetermined signal range based on the signal strength of AP (Access Point) having the strongest signal strength among the WiFi signals received at the current location are selected as a key, and the selected key is used at the current point And calculates a signal intensity error (Error) of the AP with respect to each of the WiFi scan data (ScanData) of the comparison point and the current point found in the DB, Is determined as an initial position (RawPos).
5. The method of claim 4,
(Max RSSI-10) from a Max RSSI (Received Signal Strength Indicator).
5. The method of claim 4,
Is calculated by the following equation (1). ≪ EMI ID = 1.0 >
(1)

here

Is the signal strength of the DB,

Is the signal strength of the scan data.
The indoor positioning module receiving WiFi signals in real time at the current location and determining an initial position (RawPos) by referring to data of a DB (Data Base) generated in advance;
The stabilization module calculating an average point for a predetermined number of accumulated initial positions (RawPos) to determine a stable position (StablePos); And
And determining a stable position (StablePos) calculated as a final position by reflecting whether a failure position is continuously generated in the step of determining the stable position by the final positioning module. Positioning method.
8. The method of claim 7, wherein determining the initial position comprises:
Collecting scan data (ScanData) by performing a Wi-Fi scan at a current location of the indoor positioning module;
Selecting APs within a predetermined range based on an AP (Access Point) having the strongest signal strength (RSSI) in the scan data (ScanData);
Selecting data of candidate points from a previously generated DB (HashMapDB) using APs selected by the Key;
Calculating an error between the AP signal of the candidate points selected in the DB (HashMapDB) and the scanned AP signal; And
And selecting a point where the calculated error is the smallest among the data of the candidate points and determining it as an initial position (RawPos).
9. The method of claim 8,
Wherein the key is a point including data that is likely to be selected as a current location, and a key selected at the current location and a location where the same key is set in the DB.
8. The method of claim 7, wherein determining the stable position (StablePos)
Receiving the positioning data for the current position from the indoor positioning module;
Calculating a distance from a current position calculated based on the positioning data to a predetermined reference position; And
And accumulating the current position in the temporary position array if the calculated distance is less than a preset distance and calculating an average point of points accumulated in the predetermined number of temporary position arrays and setting the stable position as a stable position Based on the location information.
11. The method of claim 10, wherein determining the stable position (StablePos)
Accumulating the current position in the failure location array when the calculated distance exceeds a predetermined distance, calculating an average point of consecutively accumulated points in the predetermined number of failure location arrays, and setting the average point as a stable location (StablePos) And determining a location of the indoor location based on the location information.
11. The method of claim 10, wherein, with respect to the reference position,
If the positioning data for the current position is the first positioning data, the current position is the reference position, and if the previously measured positioning data exists, the final position or stable position calculated using the positioning data is the reference position Wherein the first fingerprint is a fingerprint-based indoor positioning method.

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