KR20140119332A - Method and Apparatus for Location Determination using Distance Measuring Algorithm - Google Patents

Method and Apparatus for Location Determination using Distance Measuring Algorithm Download PDF

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KR20140119332A
KR20140119332A KR1020130034035A KR20130034035A KR20140119332A KR 20140119332 A KR20140119332 A KR 20140119332A KR 1020130034035 A KR1020130034035 A KR 1020130034035A KR 20130034035 A KR20130034035 A KR 20130034035A KR 20140119332 A KR20140119332 A KR 20140119332A
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location
user
data
technique
final
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이창호
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인텔렉추얼디스커버리 주식회사
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Priority to KR1020130034035A priority Critical patent/KR20140119332A/en
Priority to PCT/KR2014/002713 priority patent/WO2014158007A1/en
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W64/00—Locating users or terminals or network equipment for network management purposes, e.g. mobility management
    • H04W64/006—Locating users or terminals or network equipment for network management purposes, e.g. mobility management with additional information processing, e.g. for direction or speed determination
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/02—Services making use of location information
    • H04W4/023—Services making use of location information using mutual or relative location information between multiple location based services [LBS] targets or of distance thresholds

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Abstract

Disclosed are a method and an apparatus for location determination using a distance measuring algorithm to improve accuracy of location information. The method for location determination using the distance measuring algorithm suggested herein includes obtaining a temporary location of a user based on signal strength data collected from an access point (AP) existing around the user; searching a location information database for location data of the temporary location; and determining whether a location map corresponding to the temporary location exists. The method further includes determining a final location of the user using a first location determination method if the location map corresponding to the temporary location exists in the location information database; and determining the final location of the user using a second location determination method if the location map corresponding to the temporary location does not exist, wherein the first and second location determination methods are different from each other. The method further includes transmitting the determined final location of the user to the location information database; and displaying the final location of the user on the location map.

Description

BACKGROUND OF THE INVENTION 1. Field of the Invention [0001] The present invention relates to a position measurement method and apparatus using a distance measurement algorithm,

The present invention relates to a position estimation method and apparatus using a distance measurement algorithm for improving the accuracy of position information.

The location information service using wireless communication technology can be implemented through various methods such as cell-ID method, trigonometric method, probabilistic modeling-based method, and screen analysis regardless of the type of wireless communication technology. As the LBS (Location Based Service) industry grows, demand for high-precision location positioning technology is increasing rapidly. In the United States and EU, a national project on GPS disturbance is underway. It has the highest level of technological competitiveness in the field of satellite communication earth station (VSAT) transmission.

Among these, probabilistic modeling is a method of estimating a location called a fingerprint method, and utilizes noise and environment information as information for position tracking. This method consists of a training step and a positioning step. In the training phase, a number of RPs (Reference Points) are set in the space to be tracked and a propagation characteristic value is taken from all the RPs to create a database. In the positioning step, the user's position is estimated by measuring the propagation characteristic value in real time for the user, finding the most similar value through the database search, and presenting the corresponding RP. The sample point can be configured as a honeycomb shape for all the space to be tracked similar to the Gell-ID method, and the RP can be set only for the points on the path where the user can actually exist. This method also improves accuracy as the number of RPs receiving the user's radio signal increases, and it is known that it is appropriate to use three or four RPs in various experiments. This method has the advantage of providing the highest accuracy because it reflects the direction of the user and environmental information including noise even in the position estimation. However, there are management problems such as the problem of collecting various radio wave characteristic values for many RPs and the necessity of newly collecting the propagation characteristic values for RP whenever environment changes such as a change of furniture arrangement occur.

On the other hand, the Rice Gaussian algorithm calculates the probability that the distance from the APi in the RPj belonging to the fingerprint method is the distance S api . To calculate the probability, the actually measured distance value at the APi corresponds to the density function of the normal distribution with the parameters of APi stored in the database. The Rice Gaussian algorithm assumes that the distance value measured at a fixed position follows a normal distribution, so that the parameter representing the characteristics of the normal distribution of the APi is averaged

Figure pat00001
And standard deviation
Figure pat00002
. Therefore, the probability that the distance value from APi becomes S api in RPj
Figure pat00003
Is expressed by Equation (1).

[Equation 1]

Figure pat00004

In Equation (1)

Figure pat00005
Where m represents the number of receivable APs,
Figure pat00006
Where n represents the number of RPs.
Figure pat00007
The
Figure pat00008
Wow
Figure pat00009
As a parameter, a density function of a normal distribution. The parameters of the density function are the mean and standard deviation of the repeated measures measured in RP and AP. In the density function of the normal distribution,
Figure pat00010
Since the probability of the input distance is zero,
Figure pat00011
. After calculating the individual probabilities for the total distance values received from the i APs in the j RPs included in the actual measurement sample, the total probability of the RPj to determine the RP most similar to the user's location
Figure pat00012
Is calculated as a product of all probabilities to have a distance value S api from m APi as shown in Equation (2).

&Quot; (2) "

Figure pat00013

At this time, the coordinates of the RP having the highest total probability value P i (s) among the n RPs are determined as the positions of the user.

In order to obtain weights of exponentially large differences rather than linear differences according to the priority, a new weight is calculated by converting a probability value to a log-scale. Therefore, the weight W j of the j-th RP can be represented as Equation (3) by using a probability value converted into a log-scale.

&Quot; (3) "

Figure pat00014

The logarithm of the logarithm of the logarithmic scale is the logarithm of the logarithm of the logarithm of the logarithm.

Figure pat00015
To prevent the denominator from becoming zero. Also, taking the base 10 is intuitively easy to understand when converting the exponential relationship of the probability value to a log-scale, and if the logarithm of the logarithmic scale is taken as a different number As a result, the same weight is derived. The left side of Equation (4) is an equation for calculating the weight of the nth RP among k RPs having a high priority. R i means an RP having an i-th priority, n is a positive integer equal to or greater than 1, and less than or equal to k.

&Quot; (4) "

Figure pat00016

When we compare the weights of deterministic algorithms with the weights of kWNN algorithms, we can confirm that the first priority RP has a large effect on the total weight. The estimated position of the MU to which the kWNN algorithm of the stochastic method is applied by using the weight can be obtained by Equation (5).

&Quot; (5) "

Figure pat00017

However, when the system is constructed using these algorithms, the calculation method is complicated and the values are converted several times. Thus, although accurate results can be provided, the system can become heavy to estimate the user's position in real time. In addition, the method using the position map can be performed in a system in which the position map data is stored in the database, and since each company has the position map data, there is a restriction that it can not be interlocked with each other.

LBS (Location Based Service) refers to all types of services related to the collection, use, and provision of location information, and refers to technologies used for services that provide users with useful functions based on location information obtained through a communication network or GPS . As LBS evolves from traditional LBS services such as logistics management and location tracking to smart LBS services such as SNS and mobile apps (App), there are many forms of service such as providing information on the surrounding area, finding a friend, Services are becoming diverse and advanced. In addition, location information is used for the emergency rescue service to protect the lives of citizens in emergency situations such as disasters and crimes.

Accordingly, it is an object of the present invention to develop a position estimation method and apparatus using a distance measurement algorithm that can improve the accuracy of position information, which is the core of LBS. For this purpose, we propose a new distance measurement algorithm that can improve the accuracy of existing distance measurement algorithm using Received Signal Strength Intensity (RSSI) ranging technique of Wi-Fi.

According to an aspect of the present invention, there is provided a method of estimating a position using a distance measurement algorithm, comprising the steps of: determining a temporary position of the user based on signal strength data collected from an access point (AP) Searching for a positional data of a temporary position in the positional information database and determining whether or not there is a positional map corresponding to the positional information of the temporary position, and if the positional information database includes a positional map corresponding to the positional data of the temporary position Determining a final position of the user by using a first position measurement method and determining a final position of the user by using a second position positioning method when there is no position map corresponding to the position data of the temporary position; The coordinates of the final position of the user are transmitted to the position information database, Group comprising displaying a user's final position, and the first positioning method and the second position positioning method is the use of different positioning methods.

The first position location method may include estimating a position using a fingerprint technique, performing training in the absence of training data, and performing positioning in the presence of training data.

Training may be performed by collecting training data, setting an RP size according to signal intensity, storing the set RP position data in a position map database, and then performing positioning. The RP size setting allows you to fine-tune the RP size with strong signal strength and to set the RP size large if the signal strength is weak.

The step of performing the positioning may determine the position of the user using the Rice Gaussian algorithm and use the probability value of the log scale as a filtering technique for reducing the scattering of the Rice Gaussian algorithm.

The probability Pij (Sapi) to be the distance value (Sapi) from APi to RPj for determining the position of the user can be expressed by Equation (1)

[Equation 1]

Figure pat00018

From here

Figure pat00019
And
Figure pat00020
Can represent the probability density function parameter of the distance.

The total probability Pj (s) of RPj for determining the RP most similar to the user's position can be expressed by Equation (2)

&Quot; (2) "

Figure pat00021

Where m can represent the number of RPs.

If there is no location map database, the signal intensity of the user is determined. If the signal intensity of the user is strong, the position estimation is performed using the triangulation method. If the signal strength is weak, the position estimation can be performed using the least squares technique . The triangulation technique and the least squares technique use the following three ranges to derive coordinates in the range as final coordinates

I)

Figure pat00022

II)

Figure pat00023

Ⅲ) convex set area with AP vertices (intersection of half spaces expressed by linear equations connecting two adjacent APs)

Where x i and y i are the coordinates of the RP, x.axis max and y.axis max can represent a predetermined range.

The second position location method derives the final coordinates using the triangulation method when the user's signal strength is strong, and when two solutions are obtained from the simultaneous equations for determining the location of the user, If the range is satisfied, the mean value of the two solutions is used for the final coordinate calculation, and only the solution satisfying the three solutions satisfying the three ranges can be used for the final coordinate calculation. In addition, when one solution is obtained in the simultaneous equations, it can be used for final coordinate calculation when the one solution satisfies all three ranges.

The method of deriving the final coordinates using the least squares technique can be used in the final coordinate calculation when one solution is obtained in the simultaneous equations for determining the position of the user and the one solution satisfies all three ranges.

The final position of the user can be determined by using the position comparison algorithm according to the signal strength, using the triangulation method and the least square method.

In one aspect, the position estimation apparatus using the distance measurement algorithm proposed in the present invention includes a position data request unit for requesting position data and collecting signal strength data of neighboring APs, and position map data of position data in a position information database A position estimating unit for performing a position estimating method according to the presence or absence of the position map data and a position coordinate transmitting unit for transmitting the coordinates of the determined position for displaying the estimated position on the position map to the position information database, Coordinates and a position of the user.

The position estimating unit performs training and positioning and performs filtering to select only a fingerprint technique performing unit for determining a position of a user using a Rice Gaussian algorithm and only approximate values of an actual user's position among x and y coordinates, If one solution is obtained from the triangulation method performing part and the simultaneous equations which use the coordinates within the predetermined three ranges in the solution of the simultaneous equations for final coordinate calculation and the one solution satisfies all three predetermined ranges And a final position determining unit for determining a final position of the user by using a position comparison algorithm based on the signal strength and a position estimated by the triangulation technique and the least squares technique have.

The position estimating unit performs a fingerprint technique through the fingerprint technique performing unit when there is position map data in the position information database. If there is no position map data in the position information database, the position estimating unit Triangulation method is performed, and if the signal strength is weak, the least squares technique can be performed through the least squares technique performing unit.

The probability of the logarithmic scale can be used as a filtering technique for reducing the scattering of the Rice Gaussian algorithm.

According to the embodiments of the present invention, it is possible to utilize the existing AP by using the Wi-Fi method for estimating the position, and it is possible to estimate the position more accurately than the conventional positioning methods because the position map is used. In addition, if the location map is configured and if there is no location map, the positioning of the location can be performed to reduce the load on the server. Rice Gaussian algorithm, which is a probabilistic algorithm, is used among the position estimation methods using the fingerprint method, and the filtering method for reducing the scattering of the positioning is performed. Therefore, the probability value selection error can be reduced have. When the triangulation method and the least squares method are used, since the process of selecting only the approximate values from the coordinates of the coordinates is performed, the capacity to be stored in the DB is decreased, and the calculation amount can be reduced because only the data within the range is extracted and calculated.

Therefore, the position estimation method and apparatus using the distance measurement algorithm can measure the position of the user more quickly and accurately, and can estimate the position of the user with the scatter reduction and filtering technique.

1 is a flowchart of a position estimation method using a distance measurement algorithm according to an embodiment.
FIG. 2 is a flowchart illustrating a process of performing a fingerprint method when there is location map data.
3 is an exemplary diagram illustrating a fingerprint technique using a Wi-Fi signal according to an embodiment.
4 is a flowchart showing a process of directly calculating position data of a user when there is no position map data.
5 is a diagram showing a configuration of a position estimation apparatus using a distance measurement algorithm.
6 is a diagram showing a configuration of a position estimating unit for estimating a position of a user according to the presence or absence of a position map database.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings.

1 is a flowchart of a position estimation method using a distance measurement algorithm according to an embodiment.

A user may request location data (110) to measure the location of the user. It is possible to collect signal strength data of each AP located nearby by requesting location data. If there is already known AP position data among the collected signal strength data, the approximate position of the AP that already knows the position data can be grasped.

After the approximate position is determined, the position data of the position can be retrieved from the position information database (DB). Through the search, it is possible to determine whether the position map data of the position data is in the position information database (DB). After the determination, there are three techniques for estimating the position of the user according to the presence or absence of the position map data of the position data.

For example, if the location map data is in a location database (DB), a fingerprint method may be used (130). The fingerprint method will be described in detail with reference to FIG.

FIG. 2 is a flowchart illustrating a process of performing a fingerprint method when there is location map data.

The location data requested by the user can be analyzed to determine the presence or absence of the training data (210). If there is training data, positioning can be performed immediately. If there is no training data, training can be performed first and then positioning can be performed.

The user can analyze the position data requested and perform training if there is no training data.

Training data may be collected 220 to perform training. The step of collecting the training data may divide the magnitude of the signal intensity into sections to determine a reference point (RP).

After dividing the magnitude of the signal intensity by the interval, the magnitude of the RP according to the signal strength can be set (230). For example, if the signal strength is strong, the size of the RP can be finely set to improve the accuracy of the position location. On the other hand, if the signal strength is weak, the RP size can be set large.

After setting the size of the RP, the position data of the corresponding RP may be stored in the location information database and the training step may be completed (240). Thereafter, a positioning step for determining the position of the user can be performed.

If there is training data, it is possible to perform positioning immediately or if training data is not available, training can be performed first and then a positioning step can be performed.

Positioning data may be collected 250 if training data is pre-stored or training data is stored during the training phase.

After collecting the positioning data, a Rice Gaussian algorithm may be used 260 to estimate the position with a fingerprint method.

A Rician Gaussian algorithm, a stochastic algorithm, can be used to determine the user's position. the equation for obtaining the probability P ij (S api ) to be the distance value S api from the j-th RP RPj to the i-th AP APi may be as shown in Equation (1).

[Equation 1]

Figure pat00024

From here,

Figure pat00025
Represents the average of the probability density function of the distance,
Figure pat00026
Can represent the standard deviation.

The equation for obtaining the total probability P j (s) of RP j for determining the RP that is most similar to the user's position may be as shown in equation (2).

&Quot; (2) "

Figure pat00027

Here, m can represent the number of APs. The total probability Pj (s) of the jth RP can be calculated as the product of all probabilities to have the distance Sapi from RPj to m APi.

A filtering using a log-scale probability value may be performed on the Rice Gaussian algorithm used for determining the user's position (270). By performing filtering using a log-scale probability value, it is possible to reduce the scattering of the positioning of the Rice Gaussian algorithm. For Rice Gaussian algorithms, it may be difficult to calculate the priority weights of each RP by computing the arithmetic sum of the probabilities, such as deterministic algorithms. Therefore, it is possible to estimate the position of the user by converting the probability value to the log scale and calculating the first water level weight value.

A fingerprint technique using a Wi-Fi signal according to an embodiment will be described with reference to FIG.

3 is an exemplary diagram illustrating a fingerprint technique using a Wi-Fi signal according to an embodiment.

If there is no training data by analyzing the position data requested by the user, training can be performed to determine a reference point (RP). After setting the size of the RP according to the signal strength, the location data of the corresponding RP can be stored in the location information database. The position data of the RP can be represented by coordinates (310). The distance probability density function parameters 320 between n RPs and m APs can be obtained using the coordinates 310 of the RP. The probability density function parameter may include an average (330) and a standard deviation (340), which are parameters representing the characteristics of the normal distribution of APi. At this time, the probability density function parameters are converted to logarithms and filtering can be performed. The probability values converted to the logarithmic scale can be used in the Rice Gaussian algorithm. In addition, a correction and filtering method for improving the RSSI (Received Signal Strength Intensity) ranging using a Wi-Fi signal can be used. The location of the user can be determined by applying the 340 probability values converted to the log scale and the correction and filtering method 350 using the Wi-Fi signal to the Rice Gaussian algorithm. The probability distribution for determining the RP most similar to the user's position can be represented by a graph 360 by using the probability values converted into the log scale and the correction and filtering method using the Wi-Fi signal.

Referring back to FIG. 1, if the location map data is not in the location database (DB), the location data of the user can be directly calculated 140 using the triangulation technique or the least squares technique.

4 is a flowchart illustrating a process of directly calculating position data of a user when there is no position map data.

When the user does not have a preset position map in the position information database when requesting the position data, the position data of the user can be directly calculated. The user's signal strength can be determined 410 to determine a method for directly calculating the location data of the user. If the signal strength of the user is determined and the signal strength is strong, the position estimation may be performed using the triangulation technique. If the signal strength is weak, the position estimation may be performed using the least squares technique.

In order to estimate the position of an accurate user using the triangulation method, coordinate filtering can be performed to select only the approximate values of the x i and y i coordinates from the actual position. Thereafter, the coordinates within the range can be derived as final coordinates using the following three ranges.

I)

Figure pat00028

II)

Figure pat00029

Ⅲ) convex set area with AP vertices (intersection of half spaces expressed by linear equations connecting two adjacent APs)

Where x i and y i are the coordinates of the RP, x.axis max and y.axis max can represent a predetermined range.

Then, when two solutions are obtained from the simultaneous equations for determining the position of the user, the two solutions can be used in the final coordinate calculation if the two solutions satisfies all of I, II, and III. On the other hand, if there is only one solution satisfying both of Ⅰ), Ⅱ) and Ⅲ in the two solutions, only the corresponding solution can be used for final coordinate calculation.

 Also, when one solution is obtained from the simultaneous equations for determining the position, one solution can be used for the final calculation if all of the solutions I, II, and III are satisfied.

When estimating the exact user position using the least squares technique, only one solution of the simultaneous equations can be obtained. Therefore, the final coordinates can be derived using the same method as that for deriving the coordinates within the range used in the triangulation method as final coordinates.

After the user's position data is directly calculated using the triangulation method and the least square method, the final position of the user can be determined using the position comparison algorithm according to the signal strength (430).

After the user's position is determined (130 or 140) using the position estimation method according to the presence or absence of the position map data of the position data, the determined position coordinates of the user can be transmitted to the position map database for display in the position map ). The user's location coordinates and the location of the user transmitted to the location map database may be finally displayed 160 in the location map.

5 is a diagram showing a configuration of a position estimation apparatus using a distance measurement algorithm.

The position estimation apparatus using the distance measurement algorithm may include a position data request unit 510, a determination unit 520, a position estimation unit 530, a position coordinate transmission unit 540, and a display unit 560.

The position data requesting unit 510 may request the position data to measure the position of the user (110). It is possible to collect signal strength data of each AP located nearby by requesting location data.

The determination unit 520 can determine the approximate position of the AP that already knows the position data when the position data of the known AP exists in the signal intensity data collected in the position data request unit 510. [ After the approximate position of the AP is grasped, the position data of the position can be retrieved from the position information database (DB). Through the search, it is possible to determine whether the position map data of the position data is in the position information database (DB). After the determination, the position estimation unit 530 may use three techniques for estimating the position of the user according to the presence or absence of the position map data of the position data.

The position estimating unit 530 performs a method of estimating the position of the user according to the presence or absence of the position map data determined by the determining unit 520. [ 6, the configuration of the position estimation unit 530 will be described in detail.

6 is a diagram showing a configuration of a position estimator 530 for estimating a position of a user according to the presence or absence of a position map database. The position estimation unit 530 may include a fingerprint technique implementation unit 610, a triangulation technique implementation unit 620, a least squares technique implementation unit 630, and a comparison unit 640.

The fingerprint technique performing unit 610 can estimate the position of the user using the fingerprint technique when the position map data is in the position information database. The fingerprint technique performing unit 610 may perform training and positioning and determine a user's position using a Rice Gaussian algorithm which is a stochastic algorithm. Also, it is possible to perform filtering using a log-scale probability value in the Rice Gaussian algorithm used for the user's position determination.

On the other hand, if the location map data is not stored in the location information database, the location data of the user can be directly calculated. If the signal intensity of the user is determined and the signal strength is strong, the triangulation technique performing unit 620 can estimate the position of the user using the triangulation technique. In addition, when the signal strength is weak, the least squares scheme performing unit 630 can estimate the position of the user using the least squares technique.

The comparing unit 640 can determine the final position of the user using the position comparison algorithm according to the signal strength, using the position of the user estimated by the triangulation technique performing unit 620 and the least squares technique performing unit 630.

The position coordinate transfer unit 540 may transmit the position coordinates of the user determined using the position estimation method according to the presence or absence of the position map data of the position data to the position information database in order to display the position coordinates in the position map .

The display unit 560 may finally display the coordinates of the user position received from the position coordinate transmitting unit 540 and the position of the user in the position map.

According to one embodiment of the present invention, a method and apparatus for estimating a position using a distance measurement algorithm can utilize an existing AP installed by using Wi-Fi among various methods of estimating a user's location, It is possible to accurately estimate the position. In addition, if the location map is configured and if there is no location map, positioning is performed in two ways, so the load on the server can be reduced. For example, when the accurate position is measured, the fingerprint technique using the position map is used. When the approximate position of the user is to be grasped, the load of the server can be reduced by comparing the triangulation method and the least squares method. The RP corresponding to the user's position can be selected using the Rice Gaussian algorithm, which is a probabilistic algorithm among the fingerprinting method's position estimation methods. Since the weighting is calculated by converting the probability value to the log scale by the filtering technique for reducing the scattering of the positioning, the probability value selection error can be reduced as compared with the conventional method. When the triangulation method and the least squares method are used, only the values approximate to the user's position among the coordinates of the RP are selected, so that the capacity stored in the DB can be reduced. Since only the data within a predetermined range is extracted and calculated, the amount of calculation can be reduced. Therefore, the position of the user can be measured quickly and accurately, and the position of the user can be accurately estimated by the scatter reduction and filtering technique.

The apparatus described above may be implemented as a hardware component, a software component, and / or a combination of hardware components and software components. For example, the apparatus and components described in the embodiments may be implemented within a computer system, such as, for example, a processor, a controller, an arithmetic logic unit (ALU), a digital signal processor, a microcomputer, a field programmable array (FPA) A programmable logic unit (PLU), a microprocessor, or any other device capable of executing and responding to instructions. The processing device may execute an operating system (OS) and one or more software applications running on the operating system. The processing device may also access, store, manipulate, process, and generate data in response to execution of the software. For ease of understanding, the processing apparatus may be described as being used singly, but those skilled in the art will recognize that the processing apparatus may have a plurality of processing elements and / As shown in FIG. For example, the processing unit may comprise a plurality of processors or one processor and one controller. Other processing configurations are also possible, such as a parallel processor.

The software may include a computer program, code, instructions, or a combination of one or more of the foregoing, and may be configured to configure the processing device to operate as desired or to process it collectively or collectively Device can be commanded. The software and / or data may be in the form of any type of machine, component, physical device, virtual equipment, computer storage media, or device , Or may be permanently or temporarily embodied in a transmitted signal wave. The software may be distributed over a networked computer system and stored or executed in a distributed manner. The software and data may be stored on one or more computer readable recording media.

The method according to an embodiment may be implemented in the form of a program command that can be executed through various computer means and recorded in a computer-readable medium. The computer-readable medium may include program instructions, data files, data structures, and the like, alone or in combination. The program instructions to be recorded on the medium may be those specially designed and configured for the embodiments or may be available to those skilled in the art of computer software. Examples of computer-readable media include magnetic media such as hard disks, floppy disks and magnetic tape; optical media such as CD-ROMs and DVDs; magnetic media such as floppy disks; Magneto-optical media, and hardware devices specifically configured to store and execute program instructions such as ROM, RAM, flash memory, and the like. Examples of program instructions include machine language code such as those produced by a compiler, as well as high-level language code that can be executed by a computer using an interpreter or the like. The hardware devices described above may be configured to operate as one or more software modules to perform the operations of the embodiments, and vice versa.

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 exemplary embodiments. For example, it is to be understood that the techniques described may be performed in a different order than the described methods, and / or that components of the described systems, structures, devices, circuits, Lt; / RTI > or equivalents, even if it is replaced or replaced.

Therefore, other implementations, other embodiments, and equivalents to the claims are also within the scope of the following claims.

Claims (15)

Determining a temporary location of the user based on signal strength data collected from an access point (AP) in the vicinity of the user;
Searching the location information database for the location data of the temporary location and determining whether there is a location map corresponding to the location data of the temporary location;
If there is a position map corresponding to the position data of the temporary position in the position information database, determines the final position of the user using the first position positioning method, and if there is no position map corresponding to the position data of the temporary position Determining a final location of the user using a second location location method; And
Transmitting coordinates of the end position of the user to the position information database and displaying the end position of the user in the position map,
Wherein the first positioning method and the second positioning method use different positioning methods
Position estimation method. The method according to claim 1,
The first positioning method estimates a position using a fingerprint technique,
Performing training if there is no training data; And
Performing positioning if there is training data
/ RTI > 3. The method of claim 2,
The step of performing the training
Collects the training data, sets the RP size according to the signal strength, stores the set RP position data in the position map database, and performs positioning
Position estimation method. The method of claim 3,
The setting of the RP size
Fine-tune the RP size with strong signal strength, and set the RP size to large if signal strength is low
Position estimation method. 3. The method of claim 2,
The step of performing positioning
A position of a user is determined using a Rice Gaussian algorithm, and a probability scale of the log scale is used as a filtering technique for reducing the scattering of the Rice Gaussian algorithm
Position estimation method. 6. The method of claim 5,
The probability P ij (S api ) to be the distance value (S api ) from AP i to RP j for determining the position of the user can be expressed by Equation (1)
[Equation 1]
Figure pat00030

From here
Figure pat00031
And
Figure pat00032
Is the probability density function parameter of distance
Position estimation method. 6. The method of claim 5,
The total probability P j (s) of the RP j for determining the RP most similar to the user's position can be expressed by Equation (2)
&Quot; (2) "
Figure pat00033

Where m is the number of APs
Position estimation method. The method according to claim 1,
The second location location method estimates the location using a triangulation technique when the user's signal strength is strong, estimates the location using the least squares technique when the signal strength is weak,
The triangulation technique and the least squares technique use the following three ranges to derive coordinates in the range as final coordinates
I)
Figure pat00034

II)
Figure pat00035

Ⅲ) convex set area with AP vertices
Where x i and y i are the coordinates of the RP, x.axis max and y.axis max are the predetermined range
Position estimation method. 9. The method of claim 8,
The method of deriving the final coordinates using the triangulation technique
When two solutions are obtained in a simultaneous equations for determining a position of a user and the two solutions satisfy all three ranges, an average value of two solutions is used for final coordinate calculation, and the three ranges Is used for the final coordinate calculation,
When one solution is obtained from the simultaneous equations, the solution is used for final coordinate calculation when the solution satisfies all three ranges.
Position estimation method. 9. The method of claim 8,
The method of deriving the final coordinates using the least squares technique
One solution is obtained from the simultaneous equations for determining the position of the user, and when one solution satisfies all of the three ranges,
Position estimation method. 9. The method of claim 8,
The final position of the user is determined using the position comparison algorithm according to the signal strength by the position estimated by the triangulation technique and the least squares technique
Position estimation method. A location data request unit for requesting location data from an access point (AP) located near the user and collecting signal strength data;
A determination unit for searching the positional data database for the positional data and determining the presence or absence of a positional map corresponding to the positional data;
If there is a position map corresponding to the position data in the position information database, determines a final position of the user using a first position positioning method, and if there is no position map corresponding to the position data, A position estimator for determining a final position of the user by using the position estimator;
A position coordinate transmitter for transmitting coordinates of the determined position to the position information database to display the estimated position on the position map; And
A display unit for displaying the position coordinates and the position of the user
. 13. The method of claim 12,
The position estimating unit
A fingerprint technique performing unit for performing training and positioning and determining a position of a user using a Rice Gaussian algorithm;
a triangulation method performing unit that performs filtering to select only x and y coordinates that are close to the actual user's position and uses the coordinates within the predetermined three ranges in the solution of the simultaneous equations for final coordinate calculation;
A least squares technique performing unit for obtaining one solution in the simultaneous equations and using the solution in the final coordinate calculation when the one solution satisfies all three predetermined ranges; And
A final position determining unit for determining a final position of the user by using a position comparison algorithm based on the signal strengths of the positions estimated by the triangulation technique and the least-
. 14. The method of claim 13,
The position estimating unit
When there is location map data in the location information database, the fingerprint technique is performed through the fingerprint technique performing unit,
If there is no location map data in the location information database, the triangulation technique is performed through the triangulation technique performing unit if the signal strength of the user is strong. If the signal strength is weak, the least squares technique is performed through the least square technique performing unit
/ RTI > 14. The method of claim 13,
As a filtering technique for reducing the scattering of the Rician Gaussian algorithm,
.
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