KR20100124649A - System and method for measuring location of mobile terminal - Google Patents

Abstract

PURPOSE: A system and a method for measuring location of a mobile terminal are provided to accurately measure the location of the mobile terminal. CONSTITUTION: A system measures RSSI(Received Signal Strength Indication) values of a plurality of radio signals transmitted to the mobile terminal(S110). Using the extended Kalman filter, the system computes the first location of the mobile terminal in the first point of time in the first point of time from received signal strength indicator values(S120). Using the particle filter, the second position of the mobile terminal is computed from received signal strength indicator values which the system is received in the first point of time in location and the second point of time of the mobile terminal in the second point of time(S130).

Description

System and method for measuring location of mobile terminal

The present invention relates to a position measuring system and a method thereof, and more particularly, to a mobile terminal capable of measuring the position of a mobile terminal in real time using a received signal strength indication (RSSI) of a radio signal. A position measuring system and method thereof are provided.

Recently, as the tendency to run a convenient life, there is a growing interest in location measurement technology that allows users to check their location in real time.

Since users generally have at least one mobile terminal, location measurement of the user can be performed using the mobile terminal.

The location measurement of the mobile terminal can be broadly classified into a location measurement using a global positioning system (GPS), a location measurement using a positioning service provided by a mobile carrier, and a location measurement using a radio frequency identification (RFID) or an infrared / ultrasound wave. have.

Position measurement technology using GPS receives a GPS signal through a mobile terminal having an antenna capable of receiving a GPS signal, calculates the current position of the user based on the received GPS signal and displays it on the screen of the mobile terminal. In this case, the mobile terminal can display the location of the user on a map prepared in advance.

The location measurement using the positioning service provided by the mobile carrier determines that the user (that is, the subscriber subscribed to the positioning service provided by the mobile carrier) is connected to the base station and determines that the base station is located in the service area of the base station. To provide.

The position measuring technology using RFID or infrared / ultrasound measures a user's current position by using infrared or ultrasonic waves or wireless signals emitted from three or more access points (APs).

However, the position measuring technique using GPS has a disadvantage in that the position measurement of the user is impossible in an area (eg, an indoor area or a dense building area) where the GPS signal cannot reach. In addition, the location measurement using the positioning service provided by the mobile carrier has a disadvantage that it is difficult to estimate the location of the user within a few meters because the service area of the base station is very wide (hundreds of meters to several kilometers). In addition, in the case of the position measuring technology using RFID, it is economically undesirable because a plurality of RFID readers must be installed in the positioning area, and in the case of the position measuring technology using infrared / ultrasound, a non line of sight signal) It is not suitable for location measurement in general indoor office environment due to the disadvantage that it cannot be used in environment.

In order to solve the problems of the prior art as described above, an object of the present invention is to provide a position measuring system and a method for accurately measuring the position of a mobile terminal moving in the indoor area.

In addition, another object of the present invention is to provide a location measurement system that allows a user having a mobile terminal to check in real time the area where he is located.

According to a preferred embodiment of the present invention to achieve the above object, a method for measuring the position of a mobile terminal, comprising the steps of: measuring the received strength value of a plurality of radio signals transmitted from the plurality of access points to the mobile terminal; Calculating a first position of the mobile terminal at the first time point using an extended Kalman filter, from received strength values of the plurality of radio signals at a first time point; And calculating a second position of the mobile terminal at the second time point by using a particle filter, from positions of the mobile terminal at the first time point and reception intensity values of the plurality of radio signals received at the second time point. A location measuring method of a mobile terminal is provided.

In this case, the method for measuring the position of the mobile terminal is performed at the third time point from the position of the mobile terminal at the second time point and the reception intensity values of the plurality of radio signals received at the third time point using the particle filter. The method may further include calculating a third position of the mobile terminal.

In addition, according to another embodiment of the present invention, a position measurement system of a mobile terminal including a plurality of access points, using an extended Kalman filter, transmitted from the plurality of access points to the mobile terminal at a first time point A first position calculator configured to calculate a first position of the mobile terminal at the first time point from received intensity values of a plurality of radio signals; And using a particle filter, from the position of the mobile terminal at the first time point and the reception intensity values of the plurality of radio signals transmitted from the plurality of access points to the mobile terminal at the second time point, at the second time point. A position measuring system of a mobile terminal is provided that includes a second position calculator for calculating a second position of the mobile terminal.

According to the present invention, it is possible to accurately measure the position of the mobile terminal moving in the indoor area.

In addition, according to the present invention, a user having a mobile terminal can check in real time the area where he is located.

Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings. In order to facilitate a thorough understanding of the present invention, the same reference numerals are used for the same means regardless of the number of the drawings.

1 is a flowchart illustrating a method for measuring a location of a mobile terminal according to an embodiment of the present invention. Hereinafter, a process performed in each step will be described in detail with reference to FIG. 1.

First, in step S110, reception strength values of a plurality of radio signals transmitted from a plurality of access points (APs) to a mobile terminal are measured.

In this case, the mobile terminal may move inside the building.

In general, since the strength of the radio signal transmitted in space decreases in proportion to the transmission distance, the received signal strength (ie, received signal strength indication (RSSI)) value of the measured radio signal is determined between the mobile terminal and the plurality of APs. Used to estimate distance. At this time, in order to measure the position of the mobile terminal, at least three APs for transmitting a radio signal should exist.

Hereinafter, referring to FIG. 2, a concept of measuring the position of the mobile terminal using a radio signal transmitted from the three APs to the mobile terminal will be described.

2 is a diagram illustrating a concept of calculating a location of a mobile terminal using RSSI values of radio signals transmitted from three APs according to an embodiment of the present invention.

When the RSSI values of the three radio signals transmitted from the three APs 210 to 230 to the mobile terminals are measured, a virtual curve representing a position where the mobile terminal is estimated to exist may be drawn around each AP. In this case, the mobile terminal is located on an intersection where three virtual curves intersect.

That is, when the AP1 210 to the AP3 230 transmit the first radio signal to the third radio signal to the mobile terminal, respectively, the first virtual curve 211 centering on the AP1 210 and the AP2 220. The second virtual curve 221 centered on the second virtual curve 231 centered on the AP3 230 may be generated. In this case, it can be estimated that the mobile terminal is located at a position where the first curve 211 to the third curve 231 intersect.

In FIG. 2, the location of the mobile terminal is measured based on radio signals transmitted from three APs 210 to 230. However, this is for convenience of description, and the number of APs transmitting radio signals is three or more. It will be apparent to those skilled in the art that this can be. As the number of APs increases, the position of the mobile terminal can be measured more accurately.

According to an embodiment of the present invention, the distance between the AP and the mobile terminal may be calculated using a path loss model represented by Equation 1 below.

는 i번째 AP와 이동 단말간의 거리,

은 환경 요인에 따른 변수,

는 i번째 AP에서 전송된 무선신호의 송신세기,

는 i번째 AP에서 전송된 무선신호의 수신세기,

는 i번째 AP에 구비된 송신 안테나의 안테나 이득,

는 이동 단말에 구비된 수신 안테나의 안테나 이득,

는 이동 단말이 위치한 지역의 구조물에 따르는 표준편차 값,

는 i번째 AP에서 전송된 무선신호의 파장을 각각 의미한다. here,

Is the distance between the i-th AP and the mobile terminal,

Is a variable based on environmental factors,

Is the transmission strength of the radio signal transmitted from the i th AP,

Is the reception strength of the radio signal transmitted from the i th AP,

Is the antenna gain of the transmit antenna provided in the i th AP,

Is the antenna gain of the receiving antenna provided in the mobile terminal,

Is the standard deviation value according to the structure of the area where the mobile terminal is located,

Denotes the wavelength of the radio signal transmitted from the i-th AP.

은 2 이상 5 이하의 자연수인 것이 바람직하고,

는 3dB 이상 20dB 이하의 실수인 것이 바람직하다. in this case,

It is preferable that it is natural number of 2 or more and 5 or less,

Is preferably a real number of 3 dB or more and 20 dB or less.

The calculation of the distance between the AP and the mobile terminal using Equation 1 may be performed in step S120 described below.

Again, a method of measuring a position of a mobile terminal according to an embodiment of the present invention will be described in detail with reference to FIG. 1.

In operation S120, an extended Kalman Filter (EMK) is used to calculate a first position of a mobile terminal at a first time point based on received strength values of a plurality of radio signals at a first time point. In operation S130, a particle filter may be used to determine the location of the mobile terminal at the first time point and the reception strength values of the plurality of radio signals received at the second time point at the second time point. Compute a second position of the mobile terminal. In operation S140, the third position of the mobile terminal at the third time point is calculated based on the position of the mobile terminal at the second time point and the reception intensity values of the plurality of radio signals received at the third time point using a particle filter. do.

That is, the method for measuring the position of a mobile terminal according to an embodiment of the present invention measures an initial position of the mobile terminal using an extended Kalman filter, and after the initial position of the mobile terminal is measured, uses a particle filter of the mobile terminal. Measure the position.

Hereinafter, the position measurement of the mobile terminal performed in step S120 will be described with reference to FIGS. 3 and 4, and the position measurement of the mobile terminal performed in steps S130 and S140 will be described with reference to FIG. 5. This will be explained.

3 is a diagram illustrating a filtering algorithm of an extended Kalman filter for measuring the position of a mobile terminal at a first time point according to an embodiment of the present invention.

In the time-variant discrete system, the basic equation of the Kalman filter is defined as a state equation represented by Equation 2 below and an observation equation represented by Equation 3 below. .

는 t시점에서의 이동 단말의 위치(즉, 이동 단말의 2차원 위치 좌표 값(x, y)의 집합 행렬, 이하 상태값이라고 한다),

는 t시점에서의 복수의 AP와 이동 단말 간의 복수의 거리 값의 집합 행렬(이하, 관측값이라고 한다)을 의미한다. 관측값

에 포함되는 복수의 거리 값은 t시점에서 수신된 복수의 무선신호의 RSSI 값을 이용하여 상기의 수학식 1에 따라 연산될 수 있다. here,

Is the position of the mobile terminal at time t (i.e., a set matrix of two-dimensional position coordinate values (x, y) of the mobile terminal, hereinafter referred to as a state value),

Denotes a set matrix (hereinafter, referred to as an observation value) of a plurality of distance values between a plurality of APs and a mobile terminal at time t. Observation

The plurality of distance values included in may be calculated according to Equation 1 using RSSI values of the plurality of radio signals received at time t.

는 t시점에서 t+1시점으로 시스템의 상태를 전이시켜주는 시간 전이 행렬,

는 t시점에서의 선형화 변환 계수 행렬,

는 상태 잡음,

는 관측 잡음을 각각 의미한다. Also,

Is a time transition matrix that transitions the state of the system from time t to time t + 1,

Is the linearized transform coefficient matrix at time t,

State noise,

Denotes the observed noise, respectively.

과 관측 잡음

은 서로 상관성이 없는 독립적인 잡음으로서, 상태 잡음

은 평균이 0이고, 분산이 Q인 가우시안 분포로 모델링될 수 있고, 관측 잡음

은 평균이 0이고 분산이 R인 가우시안 분포로 모델링될 수 있다. State noise

And observed noise

Are independent noises that are not correlated with each other.

Can be modeled as a Gaussian distribution with mean zero, variance Q, and observed noise

Can be modeled as a Gaussian distribution with a mean of 0 and a variance of R.

That is, the method for measuring the position of the mobile terminal according to an embodiment of the present invention calculates the initial position of the mobile terminal based on the state equation and the observation equation as shown in Equation (2).

The filtering process of the Kalman filter can be divided into two stages: a prediction stage and an update stage.

First, in the prediction step, the state value at the present time is predicted using the state value at the previous time. In addition, the prediction step estimates the variance of the traveling noise at the current time using the variance of the moving noise of the previous time, and calculates the Kalman gain at the current time using the variance of the predicted moving noise and the variance of the observed noise. . This may be expressed as Equation 4 to Equation 6 below.

는 t시점에서의 변수 값들을 이용하여 예측된 t+1시점에서의 상태값,

는 t시점에서의 변수 값들을 이용하여 예측된 t시점에서의 상태값,

는 t시점에서의 변수 값들을 이용하여 예측된 t+1시점에서의 진행 잡음의 분산,

는 t+1시점에서의 변수 값들을 이용하여 예측된 t+1시점에서의 관측 잡음의 분산,

는 t시점에서의 상태 잡음의 분산,

는 t+1시점에서의 칼만 이득,

는 t+1시점에서의 관측 잡음의 분산, 윗첨자 T는 트랜스포즈(transpose)를 각각 의미한다. 이 때, t시점은 이전 시점을, t+1시점은 현재 시점을 각각 의미한다. here,

Is the state value at time t + 1 predicted using the variable values at time t,

Is the state value at time t predicted using the variable values at time t,

Is the variance of the running noise at time t + 1 predicted using the variable values at time t,

Is the variance of the observed noise at time t + 1 predicted using the variable values at time t + 1,

Is the variance of state noise at time t,

Is the Kalman gain at time t + 1,

Is the variance of the observed noise at time t + 1, and the superscript T means transpose. In this case, time t means a previous time point and time t + 1 means a current time point.

Next, in the updating step, a difference value between the observed value obtained at the present time point and the previously predicted state value is obtained, and the state value at the present time point is finally calculated by reflecting the difference value in the predicted state value.

In addition, the update step updates the variance of the observed noise to predict the state value at the next time point.

The calculation of the state value and the update of the variance of the observed noise performed in the updating step may be expressed as in Equations 7 and 8 below.

는 최종적으로 연산된 현재 시점에서의 상태값,

는 현재 시점에서의 칼만 이득 값,

는 현재 시점에서의 관측값,

는 업데이트된 관측 잡음의 분산, 는 단위 행렬을 각각 의미한다. 앞서 설명한 바와 마찬가지로, 관측값

에 포함되는 복수의 거리 값은 t+1시점에서 수신된 복수의 무선신호의 RSSI 값을 이용하여 상기의 수학식 1에 따라 연산될 수 있다. here,

Is the state value at the current point in time,

Is the Kalman gain at this point,

Is the observation at the present time,

Is the updated variance of observed noise, Denotes an identity matrix, respectively. As previously explained, the observations

The plurality of distance values included in may be calculated according to Equation 1 using RSSI values of the plurality of radio signals received at time t + 1.

Thereafter, the prediction step and the update step are repeatedly performed to calculate the state value recursively.

The Extended Kalman Filter transforms the nonlinear equations defining the nonlinear system into Taylor-series for use of the Kalman Filter described above in a nonlinear system, and the equations 2 through The filter is estimated by applying Equation 8.

As described above, Extended Kalman filtering calculates the final state value at the present time by using the state value predicted at the present time and the observation at the present time. If the extended Kalman filtering is applied to the observed values, the influence of noise remains large, resulting in a large error in the finally calculated state value.

Accordingly, the position measurement system of the mobile terminal according to an embodiment of the present invention performs filtering using a modified extended Kalman filter (hereinafter, referred to as a Robust Extended Kalman Filter (Robust EKF)). By doing so, it is possible to reduce the error occurring in the finally calculated state value.

Referring to Figure 3, the extended Kalman filtering according to an embodiment of the present invention will be described in detail.

및 칼만 이득 값

을 연산한다. First, in step S121, the weight at the present time

And Kalman gain values

Calculate

The weight is a value that is reflected in the Kalman gain value in order to reduce the error in calculating the state value.

That is, according to an embodiment of the present invention, in step S121, the current time point according to the degree of noise included in the observed value of the previous time point obtained from the reception strength (RSSI) values of the plurality of radio signals received at the previous time point. Determine the weight at and calculate the Kalman gain value at the current time by reflecting the determined weight at the current time. At this time, the weight at the current time may be determined to be inversely proportional to the noise included in the observation value of the previous time.

As an example, the Kalman gain value reflecting the weight and the weight may be expressed as in Equations 9 and 10 below.

는 현재 시점에서의 가중치를 의미한다. here,

Denotes a weight at the present time.

에 포함된 잡음이 적은 경우, 수학식 10의 분모 값이 작아져서, 가중치

의 값이 커지게 되고, 이에 따라, 칼만 이득 값

역시 커지게 된다. 반대로, 관측값

에 포함된 잡음이 큰 경우, 수학식 10의 분모 값이 커져서, 가중치

의 값이 작아지게 되고, 이에 따라, 칼만 이득 값

역시 작아지게 된다. Referring to Equations 9 and 10, the observation value

When the noise included in the denominator is small, the denominator value of Equation 10 becomes small, so that the weight

The value of becomes large, and accordingly, the Kalman gain value.

It also becomes large. Conversely, observations

When the noise included in the signal is large, the denominator value in Equation 10 becomes large, resulting in a weight.

The value of becomes small and accordingly, the Kalman gain value

Too small.

및 가중치

는 임의의 값을 가질 수 있다. 일례로서, 가중치

의 초기값은 1인 것이 바람직하다. In the early stages, the variance of the observed noise from earlier time points

And weights

May have any value. As an example, weights

It is preferable that the initial value of is 1.

을 이용하여, 상태값

을 업데이트하여 최종적으로 현재 시점에서의 이동 단말의 상태값

을 연산한다. 상태값

은 상기의 수학식 7에 따라 연산될 수 있다. 이 경우, 현재 시점에서의 관측값

는 현재 시점에서 수신된 복수의 무선신호의 RSSI 값을 이용하여 상기의 수학식 1에 따라 연산된 값이다. 또한, 상태값

은 아래에서 설명할 단계(S130)의 입력값이 된다. Thereafter, in step S122, the Kalman gain value calculated according to Equation 9 above.

State value

To update the status value of the mobile terminal at the present time

Calculate Status value

May be calculated according to Equation 7 above. In this case, the observation at the present time

Is a value calculated according to Equation 1 using RSSI values of the plurality of radio signals received at the present time. Also, status value

Is the input value of step S130 to be described below.

은 이전 시점에서 연산된 상태값

를 이용하여 예측될 수 있는데, 초기 단계에서, 상태값

이 존재하지 않으므로, 상태값

은 예측될 수 없다. 따라서, 초기 단계에서의 상태값

은 임의의 값을 가질 수 있다.As previously explained, the state value

Is the state value computed from the previous point

It can be predicted using

Since this does not exist, the state value

Cannot be predicted. Thus, the state value at the initial stage

May have any value.

을 업데이트하여 다음 시점에서의 상태값 측정에 사용될 관측 잡음의 분산

을 연산한다. In step S123, dispersion of the observed noise

To update the variance of the observed noise to be used to measure the state value at

Calculate

및 관측 잡음의 분산

을 이용하여 다음 시점에서의 상태값

의 연산에 이용될 상태값

및 관측 잡음의 분산

을 예측한다. 예측된 상태값

및 관측 잡음의 분산

은 단계(S121)의 입력값이 되고, 이에 따라, 이동 단말의 상태값이 재귀적으로 연산된다. Finally, in step S124, the state value

And variance of observed noise

Status value at the next time using

State value to be used for operation of

And variance of observed noise

To predict. Expected state value

And variance of observed noise

Is the input value of step S121, whereby the state value of the mobile terminal is recursively calculated.

According to an embodiment of the present invention, steps S121 to S124 for calculating the initial position of the mobile terminal (ie, the first position of the mobile terminal at the first time point) may be performed only once. Plural times may be performed. When the steps S121 to S124 are performed a plurality of times, the initial position of the mobile terminal becomes the output value of the step S122 at the time when it is finally performed.

4 is a view for comparing the performance of the Robust extended Kalman filter and the general extended Kalman filter.

In FIG. 4, a change in distance error with time is shown graphically. Referring to FIG. 4, it can be seen that the distance error of the measured position is further reduced when using the Robust Extended Kalman Filter than when measuring the position of the mobile terminal using the general Extended Kalman Filter.

Referring to FIG. 1 again, a method of measuring a position of a mobile terminal according to an embodiment of the present invention will be described.

In operation S130, a particle filter (PF) is used to determine the position of the mobile terminal at the first time point and the reception strength (RSSI) values of the plurality of radio signals received at the second time point. Compute the second position of the mobile terminal. Subsequently, in step S140, a particle filter is used to determine the mobile terminal at the third time point from the position of the mobile terminal at the second time point and the reception strength (RSSI) values of the plurality of radio signals received at the third time point. Compute the third position.

That is, in the method for measuring the position of the mobile terminal according to an embodiment of the present invention, the initial position of the mobile terminal is calculated using the extended Kalman filter, and in the subsequent step, the position of the mobile terminal is calculated using the particle filter.

Hereinafter, referring to FIG. 5, location measurement of the mobile terminal performed in steps S130 and S140 will be described.

5 is a diagram illustrating a filtering algorithm of a particle filter for measuring a position of a mobile terminal at a time after the first time point according to an embodiment of the present invention.

The particle filter is a filter mainly used in a system having an error characteristic of a nonlinear / nonnormal distribution, and may provide information about a probability distribution of an error even when an error of the nonnormal distribution occurs.

In the particle filter, the state value and the observed value of the mobile terminal may be represented by Equations 11 and 12 below.

는 t+1시점(즉, 현재 시점)에서의 이동 단말의 상태값,

는 t시점(즉, 이전 시점)에서의 이동 단말의 상태값,

는 t+1시점에서의 이동 단말의 x축 위치,

는 t+1시점에서의 이동 단말의 x축 속도,

는 t+1시점에서의 이동 단말의 x축 가속도

는 t+1시점에서의 이동 단말의 y축 위치,

는 t+1시점에서의 이동 단말의 y축 속도,

는 t+1시점에서의 이동 단말의 y축 가속도,

는 관측 시간 간격,

는 t+1시점에서의 이동 단말의 관측값,

는 비선형 함수를 각각 의미한다. 이 때, 관측값

은 t+1시점에서 수신된 복수의 무선신호의 RSSI 값을 포함한다. here,

Is the state value of the mobile terminal at time t + 1 (ie, current time point),

Is the state value of the mobile terminal at time t (ie, the previous time),

Is the x-axis position of the mobile terminal at time t + 1,

Is the x-axis velocity of the mobile terminal at time t + 1,

Is the x-axis acceleration of the mobile terminal at time t + 1

Is the y-axis position of the mobile terminal at time t + 1,

Is the y-axis velocity of the mobile terminal at time t + 1,

Is the y-axis acceleration of the mobile terminal at time t + 1,

Is the observation time interval,

Is the observed value of the mobile terminal at time t + 1,

Are nonlinear functions, respectively. At this time, the observed value

Includes RSSI values of the plurality of radio signals received at time t + 1.

는 하기의 수학식 13과 같이 표현된다. Also,

Is expressed by Equation 13 below.

는 시간 간격에 동작하는 시간 상수를 의미한다. here,

Denotes a time constant operating at time intervals.

Referring to this, the filtering operation performed in the particle filter is described in detail as follows.

First, in step S131, weights to be applied to each of a plurality of particles generated in step S132 described below are initialized.

로 초기화될 수 있다. 파티클 필터는 재귀적(recursive)으로 이동 단말의 위치를 추정하므로, 단계(S131)은 파티클 필터를 이용하여 최초로 이동 단말의 위치를 연산하는 경우에만 수행된다. In one example, the plurality of weights

Can be initialized to Since the particle filter estimates the position of the mobile terminal recursively, step S131 is performed only when the position of the mobile terminal is first calculated using the particle filter.

In step S132, a plurality of particles are generated based on the initial position of the mobile terminal output from step S120.

For example, the positions of the plurality of particles may be determined according to a probability distribution of a normal distribution that averages an initial position of the mobile terminal.

을 예측한다. 파티클 필터를 이용하여 이동 단말의 위 치를 최초로 연산하는 경우, 상태값

은 단계(S120)에서 출력된 이동 단말의 상태값으로부터 연산된다.In step S133, the position of the mobile terminal at the present time, that is, the state value, using Equation 12 described above.

To predict. State value when the position of the mobile terminal is first calculated using the particle filter

Is calculated from the state value of the mobile terminal output in step S120.

및 이전 시점에서의 가중치를 이용하여 현재 시점에서의 가중치를 연산한다. 이는 하기의 수학식 14와 같이 표현될 수 있다. In step S134, the state value calculated in step S133.

And calculate the weight at the current time using the weight at the previous time. This may be expressed as in Equation 14 below.

는 현재 시점에서 j번째 파티클에 적용되는 가중치,

는 이전 시점에서 j 번째 파티클에 적용되는 가중치,

는 t+1시점까지의 관측값(즉,

내지

),

는 조건부 확률 밀도 함수를 각각 의미한다. 이 때, 조건부 밀도 함수

는 하기의 수학식 15과 같이 표현될 수 있다. here,

Is the weight applied to the jth particle at this time,

Is the weight applied to the j th particle at the previous point,

Is an observation up to the point t + 1 (that is,

To

),

Denotes each conditional probability density function. Conditional density function

May be expressed as in Equation 15 below.

는 정규화 상수(normalizing constant),

는 j번째 파티클의 사후 확률을 각각 의미한다. here,

Is a normalizing constant,

Denotes the posterior probability of the j th particle, respectively.

을 연산한다. In step S135, the position values of the plurality of particles are multiplied by the weights calculated in step S134, averaged, and finally the state values of the mobile terminal at the present time.

Calculate

및 단계(S135)에서 연산된 상태값

은 단계(S132)의 입력값이 된다. Weight calculated in step S134

And the state value calculated in step S135.

Becomes the input value of step S132.

Referring to FIG. 1 again, a position measuring apparatus of a mobile terminal according to an embodiment of the present invention will be described.

In step S140, a third position of the mobile terminal at the third time point is calculated from the position of the mobile terminal at the second time point and the reception intensity values of the plurality of radio signals received at the third time point using a particle filter. do.

That is, after the initial position of the mobile terminal (ie, the first position of the mobile terminal) is calculated, the position measuring apparatus of the mobile terminal measures the position of the mobile terminal in real time through particle filtering.

Since the operation performed in step S140 is the same as in step S130 described above, a description thereof will be omitted.

In addition, embodiments of the present invention may be implemented in the form of program instructions that may be executed by various computer means to be recorded on a computer readable medium. The computer readable medium may include program instructions, data files, data structures, etc. alone or in combination. Program instructions recorded on the media may be those specially designed and constructed for the purposes of the present invention, or they may be of the kind well-known and available to those having skill in the computer software arts. Examples of computer-readable recording media include magnetic media such as hard disks, floppy disks, and magnetic tape, optical media such as CD-ROMs, DVDs, and magnetic disks, such as floppy disks. Examples of program instructions such as magneto-optical, ROM, RAM, flash memory, etc. may be executed by a computer using an interpreter as well as machine code such as produced by a compiler. Contains high-level language codes. The hardware device described above may be configured to operate as one or more software modules to perform the operations of one embodiment of the present invention, and vice versa.

6 is a block diagram illustrating a detailed configuration of a system for measuring a position of a mobile terminal according to an embodiment of the present invention.

The position measuring system 600 according to the exemplary embodiment of the present invention includes a first position calculator 610 and a second position calculator 620. Hereinafter, an operation performed for each component will be described.

First, the first position calculating unit 610 uses an extended Kalman filter to generate a first position of the mobile terminal at a first time point based on reception strength values of a plurality of radio signals transmitted from the plurality of access points to the mobile station at a first time point. Calculate the position

Since the operation of the first location calculator 610 corresponds to the operation performed in step S120 of the location tracking method of the mobile terminal, a detailed description thereof will be omitted.

Next, the second position calculating unit 620 uses a particle filter to determine the position of the mobile terminal at the first time point and the reception strength values of the plurality of radio signals transmitted from the plurality of access points to the mobile terminal at the second time point. Compute a second position of the mobile terminal at the second time point.

Since the operation performed by the second position calculator 620 also corresponds to the operation performed in the step S130 and the step S140 of the position measuring method of the mobile terminal described above, a detailed description thereof will be omitted.

The position measurement system of the mobile terminal according to an embodiment of the present invention may be installed on the mobile terminal or may be installed separately from the mobile terminal.

For example, when the position measuring system of the mobile terminal is installed on the mobile terminal, the mobile terminal directly measures the reception intensity values of the plurality of radio signals received. In addition, the mobile terminal displays its calculated position in real time to the user through the display unit provided in the mobile terminal.

As another example, when the location measurement system of the mobile terminal is installed separately from the mobile terminal, that is, installed in the positioning server for measuring the location of the mobile terminal, the positioning server is a reception strength value of a plurality of radio signals from the mobile terminal Is received, and calculates the location of the mobile terminal using the received information. The calculated position of the mobile terminal is transmitted to the mobile terminal, and the mobile terminal displays it in real time on the display unit.

 As described above, the present invention has been described by specific embodiments such as specific components and the like. For those skilled in the art to which the present invention pertains, various modifications and variations are possible. Therefore, the spirit of the present invention should not be limited to the described embodiments, and all of the equivalents or equivalents of the claims as well as the claims to be described later will belong to the scope of the present invention. .

1 is a flowchart illustrating a method for measuring a position of a mobile terminal according to an embodiment of the present invention.

2 is a diagram for explaining a concept of calculating a location of a mobile terminal using RSSI values of radio signals transmitted from three APs according to one embodiment of the present invention;

3 is a diagram illustrating a filtering algorithm of an extended Kalman filter for measuring a position of a mobile terminal at a first time point according to an embodiment of the present invention.

4 is a diagram for comparing the performance of a Robust extended Kalman filter and a general extended Kalman filter.

5 is a diagram illustrating a filtering algorithm of a particle filter for measuring the position of a mobile terminal at a time after the first time point according to an embodiment of the present invention.

6 is a block diagram showing a detailed configuration of a position measurement system of a mobile terminal according to an embodiment of the present invention.

Claims (8)

In the position measuring method of a mobile terminal, Measuring reception intensity values of a plurality of radio signals transmitted from a plurality of access points to the mobile terminal; Calculating a first position of the mobile terminal at the first point of time from the received intensity values of the plurality of radio signals received at a first time point using an extended Kalman filter; And Using a particle filter, a second position of the mobile terminal at the second time point is calculated from the position of the mobile terminal at the first time point and the reception intensity values of the plurality of radio signals received at the second time point. step Position measurement method of a mobile terminal comprising a. The method of claim 1, Computing the third position of the mobile terminal at the third time point from the position of the mobile terminal at the second time point and the reception intensity values of the plurality of radio signals received at the third time point using the particle filter. step Position measuring method of a mobile terminal further comprising. The method of claim 1, Computing the first position Computing a plurality of distance values between the plurality of access points and the mobile terminal from the received strength values of the plurality of radio signals at the first time point, and using the extended Kalman filter, from the plurality of distance values Compute position, The extended Kalman filter uses a Kalman gain value weighted according to a degree of noise included in a plurality of distance values. The method of claim 3, The weight and the weighted Kalman gain value are represented by the following equation.

here,

Is the Kalman gain at time t + 1,

Is the weight at t + 1,

Is the variance of the observed noise at time t + 1 predicted using the variable values at time t,

Is the linearized transform coefficient matrix at time t,

Is the variance of the observed noise at time t,

Is the variance of the observed noise at time t + 1,

Is a matrix of a plurality of distance values between a plurality of access points and a mobile terminal at time t,

Denotes the position of the mobile terminal at time t + 1 predicted using the variable values at time t. The method of claim 1, Wherein the mobile terminal is a method of measuring the position of the mobile terminal, characterized in that for moving inside the building. The method of claim 1, Computing the first position A plurality of distance values between the plurality of access points and the mobile terminal are calculated from the received strength values of the plurality of radio signals using the following equation, and the first value is calculated from the plurality of distance values using the extended Kalman filter. A position measuring method of a mobile terminal, characterized in that for calculating one position.

here,

Is the distance between the i th access point and the mobile terminal,

Is a variable based on environmental factors,

Is the transmission strength of the radio signal transmitted from the i th access point,

Is the reception strength of the radio signal transmitted from the i th access point,

Is the antenna gain of the transmit antenna provided in the i th access point,

Is an antenna gain of the receiving antenna provided in the mobile terminal,

Is a standard deviation value according to the structure of the area where the mobile terminal is located,

Denotes wavelengths of radio signals transmitted from the i th access point, respectively. The method of claim 6, remind

Is a natural number of 2 or more and 5 or less, remind

Is a real number of 3dB or more and 20dB or less. In the position measurement system of a mobile terminal including a plurality of access points, A first position for calculating a first position of the mobile terminal at the first time point by using an extended Kalman filter, from a reception strength value of a plurality of radio signals transmitted from the plurality of access points to the mobile terminal at a first time point A position calculator; And By using a particle filter, the position at the second time point from the position of the mobile terminal at the first time point and the reception intensity values of the plurality of radio signals transmitted to the mobile terminal from the plurality of access points at the second time point. A second position calculator for calculating a second position of the mobile terminal Position measurement system of a mobile terminal comprising a.

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