KR20210084188A - A method and apparatus for indoor location tracking using VLC - Google Patents

Abstract

The present invention relates to an indoor location tracking method using visible light communication (VLC), which uses VLC in an indoor environment to measure a location of a mobile terminal accurately, and an apparatus thereof. According to one embodiment of the present invention, a method for operating an access point (AP) comprises the following steps of: (a) acquiring channel impulse response (CIR) values for a plurality of sample points in a VLC space; (b) acquiring a CIR value for a terminal in the VLC space; and (c) estimating the location of the terminal on the basis of the CIR values for the plurality of sample points and the CIR values for the terminal.

Description

A method and apparatus for indoor location tracking using VLC {A method and apparatus for indoor location tracking using VLC}

The present invention relates to an indoor location tracking method using VLC, and more particularly, to a real-time indoor location tracking method of a mobile user using an AI engine in an environment using VLC.

A location based service (LBS) is a technology that provides a variety of useful information and services to a user based on a user's location. In recent years, as the spread of smart devices is activated, various location-based services are being made by using a positioning technology using a Global Positioning Service (GPS), a Wi-Fi Access Point (AP), or a mobile communication base station.

In the conventional positioning service, a technique using a GPS signal is mainly used, but its application is limited in an indoor environment. In the case of a positioning method using GPS, it is difficult to receive a GPS signal in an indoor environment, so accurate positioning is almost impossible. In addition, a positioning method using a received signal strength indicator (RSSI) of a Wi-Fi signal or a mobile communication signal is also used in a building. There is a problem in that positioning becomes inaccurate due to a large pseudorange error due to signal attenuation by walls, signal distortion by indoor obstacles, or multipath generation due to signal reflection.

[Patent Document 1] Korean Patent Publication No. 10-2019-0134231

The present invention was created to solve the above problems, and an object of the present invention is to provide an indoor location tracking method and apparatus using VLC.

Another object of the present invention is to provide a method and apparatus for building a fingerprint database based on a channel impulse response (CIR) value.

Another object of the present invention is to provide a method and apparatus for estimating the location of an initial terminal by applying a fuzzy matching algorithm based on the CIR value of the terminal and the collected CIR fingerprint database.

Another object of the present invention is to provide a method and apparatus for improving the accuracy of an estimated position obtained through a fuzzy matching algorithm using an improved Kalman filter.

Objects of the present invention are not limited to the objects mentioned above, and other objects not mentioned will be clearly understood from the description below.

In order to achieve the above objects, a method of operating an access point according to an embodiment of the present invention includes (a) obtaining a channel impulse response (CIR) value for a plurality of sample points in a VLC (Visible Light Communication) space. step; (b) obtaining a CIR value for the terminal in the VLC space; and (c) estimating the location of the terminal based on the CIR values for the plurality of sample points and the CIR values for the terminal.

In an embodiment, the step (c) may include determining at least one sample point having a CIR value closest to the CIR value for the terminal among the plurality of sample points.

In an embodiment, the step (c) may include estimating the location of the terminal using the CIR value for the at least one sample point.

In an embodiment, the step (c) may include determining a region of interest for performing a particle swarm optimization (PSO) algorithm based on the estimated location of the terminal.

In an embodiment, the step (c) may include re-estimating the location of the terminal by performing a PSO algorithm on the region of interest.

In an embodiment, the step (c) includes: when the terminal moves, estimating the moved location of the terminal by performing Kalman filtering based on the re-estimated location of the terminal can do.

In an embodiment, the access point device includes: a communication unit that obtains Channel Impulse Response (CIR) values for a plurality of sample points in a Visible Light Communication (VLC) space, and obtains a CIR value for a terminal in the VLC space; and a controller for estimating the location of the terminal based on the CIR values for the plurality of sample points and the CIR values for the terminal.

In an embodiment, the controller may determine at least one sample point having a CIR value closest to the CIR value for the terminal among the plurality of sample points.

In an embodiment, the controller may estimate the location of the terminal by using a CIR value for the at least one sample point.

In an embodiment, the controller may determine a region of interest for performing a particle swarm optimization (PSO) algorithm based on the estimated location of the terminal.

In an embodiment, the controller may re-estimate the location of the terminal by performing a PSO algorithm on the region of interest.

In an embodiment, when the terminal moves, the controller may estimate the moved location of the terminal by performing Kalman filtering based on the re-estimated location of the terminal.

Specific details for achieving the above objects will become clear with reference to the embodiments to be described in detail below in conjunction with the accompanying drawings.

However, the present invention is not limited to the embodiments disclosed below, but may be configured in various different forms, and those of ordinary skill in the art to which the present invention belongs ( Hereinafter, "a person skilled in the art") is provided to fully inform the scope of the invention.

According to an embodiment of the present invention, it is possible to more accurately measure the location of a mobile terminal using VLC communication in an indoor environment.

In addition, according to an embodiment of the present invention, there is no additional cost for equipment because the existing LED lights are used.

In addition, according to an embodiment of the present invention, it is possible to improve the location accuracy of a moving terminal and provide an indoor navigation service through improved Kalman filtering.

The effects of the present invention are not limited to the above-described effects, and potential effects expected by the technical features of the present invention will be clearly understood from the following description.

1 is a diagram illustrating an indoor location tracking system according to an embodiment of the present invention.
2 is a diagram illustrating a CIR value according to an embodiment of the present invention.
3A is a diagram illustrating measurement of a CIR value of a terminal according to an embodiment of the present invention.
3B is a diagram illustrating sample point selection according to an embodiment of the present invention.
3C is a diagram illustrating terminal location estimation according to an embodiment of the present invention.
3D is a diagram illustrating setting of a region of interest according to an embodiment of the present invention.
3E to 3I are diagrams illustrating execution of a POS algorithm according to an embodiment of the present invention.
3J and 3K are diagrams illustrating terminal location estimation according to an embodiment of the present invention.
3L is a diagram illustrating sample point addition according to an embodiment of the present invention.
3M is a diagram illustrating location tracking when a terminal moves according to an embodiment of the present invention.
4 is a diagram illustrating a method of operating an access point according to an embodiment of the present invention.
5 is a diagram illustrating a functional configuration of an access point according to an embodiment of the present invention.
6A is a diagram illustrating a position tracking error graph according to an embodiment of the present invention.
6B is a diagram illustrating a position tracking error graph in various environments according to an embodiment of the present invention.
6C is a diagram illustrating a location tracking error graph in a WiFi environment according to an embodiment of the present invention.

Since the present invention can have various changes and can have various embodiments, specific embodiments are illustrated in the drawings and described in detail.

Various features of the invention disclosed in the claims may be better understood upon consideration of the drawings and detailed description. The apparatus, methods, preparations, and various embodiments disclosed herein are provided for purposes of illustration. The disclosed structural and functional features are intended to enable those skilled in the art to specifically practice the various embodiments, and are not intended to limit the scope of the invention. The terms and sentences disclosed are for the purpose of easy-to-understand descriptions of various features of the disclosed invention, and are not intended to limit the scope of the invention.

In describing the present invention, if it is determined that a detailed description of a related known technology may unnecessarily obscure the gist of the present invention, the detailed description thereof will be omitted.

Hereinafter, an indoor location tracking method and apparatus using VLC according to an embodiment of the present invention will be described.

1 is a diagram illustrating an indoor location tracking system 100 according to an embodiment of the present invention. 2 is a diagram illustrating a CIR value according to an embodiment of the present invention.

Referring to FIG. 1 , the indoor positioning system 100 may include at least one access point (AP) 110 , a plurality of sample points 120 , and a terminal 130 . .

In this case, each access point 110 may obtain a channel impulse response (CIR) value for a plurality of sample points 120 in a visible light communication (VLC) space. In this case, CIR values as shown in FIG. 2 may be measured, and each access point 110 may store CIR values for a plurality of sample points 120 in a database using a fingerprint method.

In addition, the access point 110 may obtain a CIR value for the terminal 130 in the VLC space. In an embodiment, the CIR value may be measured as in Equation 1 below.

는 i번째 광선의 전파 시간이고,

는 i 번째 광선의 광 출력이고,

는 Dirac 델타 함수이고,

은 검출기에서 수신된 광선의 수를 나타낸다. here

is the propagation time of the ith ray,

is the light output of the ith ray,

is the Dirac delta function,

denotes the number of rays received at the detector.

Thereafter, the access point 110 may estimate the location of the terminal 130 based on the CIR values for the plurality of sample points 120 and the CIR values for the terminal 130 .

In an embodiment, the access point 110 may perform a weighted fuzzy matching algorithm based on the CIR values for the plurality of sample points 120 and the CIR values for the terminal 130 .

In order to perform the weighted fuzzy matching algorithm, a fingerprint map may be constructed first. The fingerprint map may be configured by creating a grid of points in an indoor area. Grid points may be displayed on the x-y coordinate plane at regular intervals. To generate a fingerprint map, a point of a sample point may be found and a CIR value between each access point 110 and the sample point 120 may be measured through <Equation 1>.

Therefore, in the case of a CIR-based location fingerprint, the entire offline database can be represented by SCIR and CIR matrices as shown in Equation 2 below.

는 CIR 기반 위치 핑거프린트를 나타내고,

는 m번째 액세스 포인트와 n번째 샘플 포인트 사이의 CIR 값을 나타낸다. 또한, 각 액세스 포인트와 샘플 포인트 사이의 벡터 값이 계산될 수 있다. 상기 벡터 값은 하기 <수학식 3>과 같이 벡터 행렬로 나타낼 수 있다. here,

represents a CIR-based location fingerprint,

denotes a CIR value between the m-th access point and the n-th sample point. Also, a vector value between each access point and the sample point may be calculated. The vector value may be expressed as a vector matrix as shown in Equation 3 below.

는 m번째 액세스 포인트와 n번째 샘플 포인트 사이의 벡터 값을 나타낸다. 임의의 위치에서 k번째 단말과 단말을 서빙하는 액세스 포인트 사이의 CIR 값은

를 얻기 위해 계산될 수 있다. 유클리드 거리(Euclidean distance)는

와

의 상관관계(correlation)를 평가하여 계산될 수 있다. 위치 k에서의 온라인 핑거프린트와 j번째 액세스 포인트의 위치 I의 오프라인 핑거프린트 사이의 상관관계는

로 주어지며, 여기서

나타낼 수 있다. 이 경우, 최소 거리는 하기 <수학식 4>와 같이 나타낼 수 있다.where D is the vector matrix,

denotes a vector value between the m-th access point and the n-th sample point. The CIR value between the k-th terminal and the access point serving the terminal at any location is

can be calculated to get Euclidean distance is

Wow

can be calculated by evaluating the correlation of The correlation between the online fingerprint at location k and the offline fingerprint at location I of the jth access point is

is given, where

can indicate In this case, the minimum distance may be expressed as in Equation 4 below.

가 산출될 수 있다. 근접도(closeness degree)는 높음에서 낮음으로 계산되며, 액세스 포인트(110)는 단말(130)과의 근접도가 더 높은 4개의 핑거프린트 벡터를 선택할 수 있다. 선택된 샘플 핑거프린트 포인트는 가까운 순서로

로 나타낼 수 있다. 그리고 단말(130)의 좌표 X0, Y0, Z0은 하기 <수학식 5>와 같이 나타낼 수 있다.Therefore, the Euclidean distance vector through <Equation 4>

can be calculated. The proximity degree is calculated from high to low, and the access point 110 may select four fingerprint vectors having a higher proximity to the terminal 130 . The selected sample fingerprint points are in nearest order.

can be expressed as And the coordinates X0, Y0, and Z0 of the terminal 130 may be expressed as in Equation 5 below.

n번째 샘플 포인트의 근접도 가중 계수(closeness degree weighted coefficient)를 나타내고,

은 하기 <수학식 6>과 같이 나타낼 수 있다. here,

Represents the closeness degree weighted coefficient of the nth sample point,

can be expressed as in Equation 6 below.

That is, according to the present invention, a fingerprinting algorithm can be used for location tracking in a LiFi environment. The fingerprint algorithm may include offline sampling and online positioning processes. The offline sampling method may include a process of constructing a sample fingerprinting database by measuring a CIR value for each sample point 120 . Also, the online positioning method may include a weighted fuzzy matching algorithm.

The fingerprinting algorithm has the advantage of using only the CIR values of the access point and sample point. Only that data can be used to measure the position, so there is no need to use an existing device or add another device.

However, there is a problem that offline measurements take a long time to build the CIR database. Also, if a variable occurs in the environment, you may need to rebuild the database. As a result, the cost of indoor positioning systems can increase significantly. Therefore, the method of reducing errors through the online positioning method as in the present invention is the most efficient. In the present invention, a weighted fuzzy matching algorithm may be used as an online positioning method. This method can reduce errors by re-tracking the exact location of the terminal using the CIR value for the location of the sample point set through the fingerprint algorithm.

3A is a diagram illustrating measurement of a CIR value of a terminal according to an embodiment of the present invention. 3B is a diagram illustrating sample point selection according to an embodiment of the present invention. 3C is a diagram illustrating terminal location estimation according to an embodiment of the present invention. 3D is a diagram illustrating setting of a region of interest according to an embodiment of the present invention. 3E to 3I are diagrams illustrating execution of a POS algorithm according to an embodiment of the present invention. 3J and 3K are diagrams illustrating terminal location estimation according to an embodiment of the present invention. 3L is a diagram illustrating sample point addition according to an embodiment of the present invention. 3M is a diagram illustrating location tracking when a terminal moves according to an embodiment of the present invention.

Referring to FIG. 3A , each access point 110 may measure a CIR value for the terminal 130 . Referring to FIG. 3B , the access point 110 has a similar CIR value based on the CIR value for the terminal measured through the fuzzy matching algorithm, that is, at least one sample point 120 closest to the terminal 130 . ) can be selected.

Referring to FIG. 3C , the access point 110 may estimate the location 310 of the terminal 130 through a fuzzy matching algorithm. Referring to FIG. 3D , the access point 110 may set a region of interest 320 for performing the PSO algorithm based on the estimated location 310 of the terminal 130 .

3E to 3I , the access point 110 may perform a PSO algorithm. In this case, the positions of the initial particles of the PSO algorithm may be randomly distributed within the region of interest 320 . In addition, in the PSO algorithm performed by the access point 110 , each particle may determine an optimal solution for the location of the terminal 130 as shown in Equation 7 below.

where c is the acceleration coefficient, r is the contraction coefficient, w is the inertia coefficient, t is the current number of iterations, T is the maximum number of iterations, pbest is the optimal position of each particle, and gbest is the optimal position of the particle cluster.

For example, in the PSO algorithm performed by the access point 110 , each particle may perform the PSO algorithm up to 10 times to find an optimal solution. Each particle can terminate the PSO algorithm when it finds an optimal solution.

3J and 3K , the access point 110 may estimate the final location 330 of the terminal 130 . Here, the final location 330 of the terminal 130 may mean a re-estimated location of the terminal. Also, the access point may use data corresponding to the final location 330 of the terminal 130 as an initial value for Kalman filtering.

Referring to FIG. 3L , the access point 110 may add a CIR value for the terminal 130 to the sample point database and use it for location tracking when the terminal 130 moves.

Referring to FIG. 3M, when the terminal 130 uses the access point 110, a more accurate estimated movement position of the terminal 130 through pre-prediction and post-prediction of the location of the terminal 130 using Kalman filtering ( 340) can be determined.

4 is a diagram illustrating a method of operating the access point 110 according to an embodiment of the present invention.

Referring to FIG. 4 , step S401 is a step of acquiring CIR values for a plurality of sample points 120 in VLC space.

Step S403 is a step of obtaining a CIR value for the terminal 130 in the VLC space.

Step S405 is a step of estimating the location of the terminal 130 based on the CIR values for the plurality of sample points 110 and the CIR values for the terminal 130 .

In an embodiment, a fuzzy matching algorithm is performed based on the CIR value for the plurality of sample points 120 and the CIR value for the terminal 130 to perform at least one sample among the plurality of sample points 120 . points can be determined.

In an embodiment, at least one sample point having a CIR value closest to the CIR value for the terminal 110 among the plurality of sample points 120 may be determined.

In an embodiment, the location of the terminal 130 may be estimated using a CIR value for at least one sample point.

In an embodiment, a region of interest for performing a particle swarm optimization (PSO) algorithm may be determined based on the estimated location of the terminal 130 .

In an embodiment, the location of the terminal 130 may be re-estimated by performing the PSO algorithm on the region of interest.

In an embodiment, when the terminal 130 moves, Kalman filtering based on the re-estimated location of the terminal 130 may be performed to estimate the moved location of the terminal 130 .

In an embodiment, the Kalman filtering may include a time update process and a measurement update process. In this case, the time update process may refer to a process of predicting the next location of the terminal 130 in advance. In addition, the measurement update may improve the accuracy of prediction by applying the actually observed position of the terminal 130 to the prior prediction.

5 is a diagram illustrating a functional configuration of an access point device 110 according to an embodiment of the present invention.

Referring to FIG. 5 , the access point device 110 may include a communication unit 510 , a storage unit 520 , and a control unit 530 .

The communication unit 510 may obtain CIR values for the plurality of sample points 120 in the VLC space and may obtain CIR values for the terminal 130 in the VLC space.

In an embodiment, the communication unit 510 may include at least one of a wired communication module and a wireless communication module. All or part of the communication unit 510 may be referred to as a 'transmitter', 'receiver', or 'transceiver'.

The storage unit 520 may store CIR values for the plurality of sample points 120 and CIR values for the terminal 130 . Also, the storage unit 520 may store the estimated location of the terminal 130 and the re-estimated location of the terminal 130 . Also, the storage unit 520 may store the re-estimated CIR value for the terminal 130 .

In an embodiment, the storage unit 520 may be configured as a volatile memory, a non-volatile memory, or a combination of a volatile memory and a non-volatile memory. In addition, the storage unit 520 may provide stored data according to the request of the control unit 530 .

The controller 530 may estimate the location of the terminal 130 based on the CIR values of the plurality of sample points 120 and the CIR values of the terminal 130 .

In one embodiment, the controller 530 may include at least one processor or microprocessor, or may be a part of the processor. Also, the controller 530 may be referred to as a communication processor (CP). The controller 530 may control the operation of the access point device 110 according to various embodiments of the present disclosure.

Referring to FIG. 5 , the access point device 110 may include a communication unit 510 , a storage unit 520 , and a control unit 530 . In various embodiments of the present invention, the access point device 110 is not essential to the configurations illustrated in FIG. 5 , so it may be implemented with more or fewer configurations than those illustrated in FIG. 5 . have.

6A is a diagram illustrating a position tracking error graph according to an embodiment of the present invention. 6B is a diagram illustrating a position tracking error graph in various environments according to an embodiment of the present invention.

Referring to FIG. 6A , when the position tracking method according to the present invention is used, the minimum position error in the VLC environment is 0.035 m, the maximum position error in the VLC environment is 3.3 m, and the average position error in the VLC environment is It can be confirmed that it is 1.1m.

Referring to FIG. 6B , location measurement may be performed in a hospital ward environment, and in this case, simulation parameters may be represented as shown in Table 1 below.

Parameter value WiFi LiFi Environment Hospital Ward Room Size 8m x 8m x 3m Number of repeated counts 10,000 Number of AP 3 9 Number of Sample Points 9 9 Power per each AP 20W 19W

The simulation is performed in a space of 8m x 8m x 3m. 16 APs can be installed per light. A relatively small number of 9 sample points can be positioned to minimize the effect of offline fingerprinting. In addition, a CIR value for a sample point may be measured by each AP. Thereafter, a fingerprinting database for the sample points may be established.

And, in the online positioning step, the terminal may have 16 APs for measuring the CIR value in the actual location coordinates indicated by the red dot. Four adjacent sample points indicated in yellow can be established. The weights of adjacent sample points can be calculated to derive the green estimated location coordinates using a weighted fuzzy matching algorithm.

6C is a diagram illustrating a location tracking error graph in a WiFi environment according to an embodiment of the present invention.

Referring to FIG. 6C , when using the conventional WiFi, the location estimation of the terminal may be performed. In an environment of the same size as LiFi according to the present invention, the AP may be installed at each location. As with LiFi, 9 sample points can be set. In addition, each RSS may measure an RSSI value for a sample point.

In addition, a fingerprinting database for sample points may be established. Each AP may measure the RSSI value between the terminals and then estimate the coordinates of the terminals using a weighted fuzzy matching algorithm. The error can be measured by comparing the terminal coordinates of the actual location with the terminal coordinates of the estimated location.

In this case, it may be performed 10,000 times by random arrangement of the terminal, and the average error of the terminal coordinates may be measured as shown in Table 2 below.

Parameter Value WiFi LiFi Average Error 2m 1.3m

As shown in <Table 2>, when estimating a location using conventional WiFi, an average error of 2m may occur. On the other hand, when estimating a location using LiFi according to the present invention, an error of 1.3 m occurs and an error reduction of 65% compared to WiFi can be confirmed.

In an outdoor environment, a location is estimated using a conventional GPS-like technique, but this technique is not suitable for indoor use. In addition, conventional WiFi, which is mainly used for location estimation in an indoor environment, requires many APs to estimate an accurate location, and thus requires a lot of cost.

However, when estimating the location using LiFi according to the present invention, it is possible to achieve low installation cost of several APs. In addition, the fingerprint technology used for location estimation requires a lot of time and effort to measure CIR and RSSI values offline. However, with LiFi according to the present invention, higher accuracy than WiFi can be achieved with fewer sample points.

In addition, as in the present invention, if a weighted fuzzy matching algorithm is used in addition to the existing fingerprinting algorithm, high accuracy can be obtained even with insufficient sample points. In addition, in the case of the present invention, the PSO algorithm and the Kalman filtering method are added to the online positioning step, so that the location estimation accuracy can be increased.

The above description is merely illustrative of the technical spirit of the present invention, and various changes and modifications may be made by those skilled in the art without departing from the essential characteristics of the present invention.

Accordingly, the embodiments disclosed in the present specification are not intended to limit the technical spirit of the present invention, but to illustrate, and the scope of the present invention is not limited by these embodiments.

The protection scope of the present invention should be interpreted by the claims, and all technical ideas within the scope equivalent thereto should be understood to be included in the scope of the present invention.

100: indoor positioning system
110: access point
120: sample point
130: terminal
310: location
320: region of interest
330: final position
340: estimated movement position
510: communication department
520: storage
530: control unit

Claims (12)

(a) obtaining CIR (Channel Impulse Response) values for a plurality of sample points in a Visible Light Communication (VLC) space;
(b) obtaining a CIR value for the terminal in the VLC space; and
(c) estimating the location of the terminal based on the CIR values for the plurality of sample points and the CIR values for the terminal;
containing,
How an access point works.
According to claim 1,
The step (c) is,
determining at least one sample point having a CIR value closest to a CIR value for the terminal among the plurality of sample points;
containing,
How an access point works.
3. The method of claim 2,
The step (c) is,
estimating the location of the terminal using the CIR value for the at least one sample point;
containing,
How an access point works.
4. The method of claim 3,
The step (c) is,
determining a region of interest for performing a Particle Swarm Optimization (PSO) algorithm based on the estimated location of the terminal;
containing,
How an access point works.
5. The method of claim 4,
The step (c) is,
re-estimating the location of the terminal by performing a PSO algorithm on the region of interest;
containing,
How an access point works.
6. The method of claim 5,
The step (c) is,
estimating the moved location of the terminal by performing Kalman filtering based on the re-estimated location of the terminal when the terminal moves;
containing,
How an access point works.
In Visible Light Communication (VLC) space, to obtain CIR (Channel Impulse Response) values for multiple sample points,
a communication unit for obtaining a CIR value for a terminal in the VLC space; and
a controller for estimating the location of the terminal based on the CIR values for the plurality of sample points and the CIR values for the terminal;
containing,
access point device.
8. The method of claim 7,
The control unit is
Determining at least one sample point having a CIR value closest to the CIR value for the terminal among the plurality of sample points,
access point device.
9. The method of claim 8,
The control unit is
Estimating the location of the terminal using the CIR value for the at least one sample point,
access point device.
10. The method of claim 9,
The control unit is
Determining a region of interest for performing a Particle Swarm Optimization (PSO) algorithm based on the estimated location of the terminal,
access point device.
11. The method of claim 10,
The control unit is
To re-estimate the location of the terminal by performing a PSO algorithm for the region of interest,
access point device.
12. The method of claim 11,
The control unit is
When the terminal moves, performing Kalman filtering based on the re-estimated location of the terminal to estimate the moved location of the terminal,
access point device.

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