KR20160009993A - Wi-Fi based method for providing position tracking and gesture recognition, and computer-readable recording medium for the same - Google Patents

Abstract

The present invention relates to a method for providing position tracking and gesture recognition using wireless LAN, and a computer-readable recording medium for the same. The present invention relates to a technology of achieving position tracking and gesture recognition by using generally wireless LAN in general and, more specifically, to a technology of tracking the position of persons moving in the vicinity of a receiver and recognizing gestures of the persons by processing a wireless LAN signal received after being reflected from an external object and detecting an angle of arrival (AoA) and Doppler shift. According to the present invention, the functions of position tracking and gesture recognition for a person moving in the vicinity of a wireless LAN signal receiver can be accurately achieved by utilizing a wireless LAN technology which can be currently implemented inexpensively.

Description

[0001] The present invention relates to a position tracking and gesture recognition method and a computer-readable recording medium for use in a wireless LAN,

Field of the Invention [0002] The present invention relates generally to techniques for achieving location tracking and motion recognition using a wireless LAN. More particularly, the present invention relates to a method for detecting a position of a person moving near a receiver by detecting an AoA (Angle of Arrival) and a Doppler shift (Doppler shift) by signal processing a wireless LAN signal received and reflected by an external object Tracking, and recognizing their actions.

Recently, there is a growing demand for a new type of computing device that interacts with a computer interface away from a desktop computer. Xbox's Kinect is a good example of an input sensor that enables motion-based mutual recognition using computer image sensors. The commercial success of these types of devices has led to the development of new user interfaces that do not require a keyboard and mouse.

Motion recognition technology can always exchange information in an available computing environment. For example, by swinging the air in the air, a person can adjust the volume of the music in the shower, change the music in the kitchen while cooking, or operate the boiler in bed just before sleep. Tracking the location of people is also possible in a computing environment. These functions can be applied to various fields including home automation, health care, and games.

However, most image-based sensor equipment is difficult to install in a house or building because of the burden and cost of equipment installation. Because of these limitations, many sensors that attach to the human body have been studied. However, since such sensors must be kept in the human body all the time, it is not an effective method in situations where the sensor can not be carried on the body, such as during a shower.

Accordingly, there is a need for a method of tracking a person's position and recognizing motion in a new form without using an image-based sensor or sensors attached to a human body.

1. Korean Patent Application No. 10-1999-0043260 "Security System and Security Method Using Wireless Communication Network"

2. Korean Patent Application No. 10-2002-0027783 entitled "Wireless audio / video security system with built-in wireless telephone &

3. Korean Patent Application No. 10-2003-0101420 "Remote security system using wireless mobile communication network and short-range wireless communication network"

4. Korean Patent Application No. 10-2002-0074915 "Home Security System"

5. Korean Patent Application No. 10-2008-0020405 entitled " Home security processing method and apparatus of home automation system "

6. Korean Patent Application No. 10-2013-0105797 entitled " Wireless Security Expense System and Method Using Wireless Electric < RTI ID = 0.0 >

7. F. Adib, Z. Kabelac, D. Katabi, and R. C. Miller, "3D Tracking via Body Radio Reflections", Usenix NSDI'14, Seattle, USA, 2014

8. Q. Pu, S. Gupta, S. Gollakota, and S. Patel, "Whole-Home Gesture Recognition Using Wireless Signals", ACM MobiCom'13, New York, USA, 2013

9. F. Adib and D. Katabi, "See Through Walls with Wi-Fi", ACM Sigcomm '13, Hong Kong, August 2013

10. "Wireless LAN Wi-Vi to monitor the wall and detect movement of people", NIPA, Weekly Technology Trend, July 17, 2013.

11. Wi-Vi. http://people.csail.mit.edu/fadel/wivi/

12. WiSee. http://www.washington.edu/news/2013/06/04/wi-fi-signals-enable-gesture-recognition-throughout-entAPe-home/

13. WiSee. http://wisee.cs.washington.edu/#

14. WHYDSP. http://www.whydsp.org/218

It is an object of the present invention to provide a technique for achieving location tracking and motion recognition using a wireless LAN in general. In particular, it is an object of the present invention to provide a technique of detecting the arrival angle and the Doppler shift by signal processing a wireless LAN signal reflected and received by an external object, thereby tracking the position of a person moving near the receiver and recognizing their operation .

According to another aspect of the present invention, there is provided a method for locating and recognizing motion using a wireless LAN, the method comprising: a first step of tracking the position of a moving object by measuring an arrival angle (AoA) of a wireless LAN signal; A second step of identifying a preset preamble operation through a generation pattern of the arrival angle; A third step of establishing synchronization between a target object and a wireless LAN receiver performing a preamble operation by searching for a channel maximizing Doppler energy of a wireless LAN signal; A fourth step of performing an FFT operation on the wireless LAN signal with the synchronization established; A fifth step of analyzing the Doppler shift for the wireless LAN signal based on the FFT operation result; And a sixth step of recognizing the operation of the target object according to the Doppler shift analysis result.

The first step of the present invention includes: obtaining a channel matrix for a wireless LAN signal; Obtaining a correlation coefficient matrix for a channel matrix; Performing eigen decomposition on the correlation coefficient matrix to obtain at least one eigenvector and setting an eigenvector matrix for the correlation matrix using the obtained eigenvector; Distinguishing a major eigenvector having a eigenvalue of a predetermined threshold value or more from a eigenvector and a minor eigenvector having an eigenvalue less than or equal to a threshold value; Identifying a constructed noise space matrix spanned by a minor eigenvector for an eigenvector matrix; Calculating an angle of incidence of the wireless LAN signal reflected by the moving object by projecting and taking a reciprocal of the noise space matrix; And tracking the position of the moving object by analyzing the arrival angle with time.

는 In the present invention,

The

은

에 의해 획득하고, 도래각

은 , And the correlation coefficient matrix

silver

And the angle of arrival

silver

은 무선랜 수신 안테나의 개수;

은 무선랜 신호의 n 시간 샘플에 p 번째 무선랜 수신 안테나로 수신된 m 번째 사용자 신호의 채널;

는 채널 행렬

의 헤르미트 행렬;

는 기댓값 연산자; θ는 노이즈 공간으로의 방향을 나타내는 각도; K는 노이즈 고유벡터의 개수;

은 노이즈 공간 행렬임.). ≪ / RTI > (At this time,

The number of WLAN receive antennas;

Channel of the m-th user signal received by the p-th WLAN receiving antenna at the n-th sample of the wireless LAN signal;

The channel matrix

The Hermite matrix of;

Is an expectation operator; [theta] is an angle indicating the direction to the noise space; K is the number of noise eigenvectors;

Is the noise space matrix.)

The third step includes acquiring an optimal complex weighting matrix for maximizing the Doppler energy of the wireless LAN signal. And projecting the WLAN signal in the direction of the target object by applying the obtained optimal complex weighting matrix to the WLAN signal.

In the present invention, the fourth step is to perform the MN-point FFT operation (where M is a preset integer and N is the number of subcarriers) for the synchronized WLAN signal so that the bandwidth of the OFDM sub- .

Meanwhile, a computer-readable recording medium according to the present invention records a program for executing a location tracking and motion recognition method using a wireless LAN in a computer.

According to the present invention, there is an advantage that a location tracking and operation recognition function for a person moving near a wireless LAN signal receiver can be accurately achieved by using a wireless LAN technology that can be implemented at a low cost.

1 is a block diagram of a WLAN receiver that performs location tracking and motion recognition according to the present invention;
FIG. 2 is a flowchart illustrating a process of recognizing a location and an operation according to the present invention;
3 is a diagram conceptually showing channels in the present invention,
4 is a view conceptually showing projection in the present invention. Fig.

Hereinafter, an operation process according to the present invention will be described in detail with reference to the drawings. The present invention is based on an OFDM-based short-range wireless communication technology. The present invention is based on a wireless LAN technology as a preferred configuration of short-range wireless communication.

1 is a block diagram illustrating a functional configuration of a WLAN receiver 100 that performs location tracking and operation recognition according to an exemplary embodiment of the present invention. Tracking and motion recognition process. The wireless LAN receiver 100 according to the present invention generally comprises a device such as a wireless LAN router, which receives a signal reflected from an external object after transmitting the wireless LAN signal to an external space, And performs position tracking and motion recognition through the phenomenon appearing, that is, Doppler shift.

1, the wireless LAN receiver 100 includes an arrival angle (AoA) measuring unit 110, a position tracking unit 120, a preamble identifying unit 130, a synchronization setting unit 140 A FFT operation unit 150, a Doppler shift analysis unit 160, a shift pattern storage unit 170, and a critical motion recognition unit 180. In addition, the wireless LAN receiver 100 according to the present invention performs a series of processes shown in FIG. 2 to achieve location tracking and operation recognition.

Hereinafter, the process of achieving location tracking and operation recognition by the wireless LAN receiver 100 will be described in detail.

First, the location of people is tracked using a wireless LAN signal. Here's how to track your location:

이라 나타낸다. [도 3]을 참조하여 살펴보면, 무선랜 신호의 n 시간 샘플에 p 번째 수신 안테나에서 수신된 m 번째 사용자(사람) 신호의 채널을

이라고 표시한다. 이때, 채널 행렬

는 다음의 [수학식 1]과 같다.
Since the signal received by the wireless LAN receiver 100 through the antenna is the sum of several signals reflected by various people, a process of decomposing the wireless LAN signal into signals reflected to each person is required. At this time, the number of WLAN receiving antennas

. Referring to FIG. 3, the channel of the m-th user (person) signal received from the p-th Rx antenna in the n-th sample of the WLAN signal

. At this time,

Is expressed by the following equation (1).

를 이용하여 상관계수 행렬(correlation matrix)

을 구하면 다음의 [수학식 2]와 같다.
Then, the channel matrix

And a correlation matrix

The following equation (2) is obtained.

는 기댓값 연산자(expectation operator)를 나타내고,

는 채널 행렬

의 헤르미트(Hermitian) 행렬, 즉 켤레전치(conjugate transpose) 행렬을 나타낸다. At this time,

Represents an expectation operator,

The channel matrix

A Hermitian matrix, i.e., a conjugate transpose matrix.

에서 노이즈(noise)를 제거하는 과정을 수행한다. 노이즈 제거를 위해 먼저 상관계수 행렬

에 대해 고유 분해(eigen decomposition)를 수행하여 복수의 고유값과 고유벡터를 획득하고, 이들 중에서 미리 설정된 임계치 이상의 고유값(eigenvalue)을 갖는 고유벡터(eigenvector)를 취한다. 본 명세서에서는 임계치 이상의 고유값을 갖는 고유벡터를 편의상 '메이저 고유벡터(major eigenvector)'라 부르고, 나머지 고유벡터를 편의상 ‘마이너 고유벡터(minor eigenvector)'라 부른다. 이 때 메이저 고유벡터의 개수가 움직이는 사람의 수에 상응한다. 예를 들어 N 명의 사람이 존재하는 상황에서는 메이저 고유벡터를 N개 얻을 수 있다.Correlation coefficient matrix

And a noise removing process is performed. To remove the noise, the correlation coefficient matrix

Eigen decomposition is performed on the eigenvectors to obtain a plurality of eigenvalues and eigenvectors, and an eigenvector having an eigenvalue equal to or greater than a preset threshold value is obtained from the eigenvectors. In this specification, an eigenvector having an eigenvalue equal to or larger than a threshold value is called a "major eigenvector" for convenience, and the remaining eigenvectors are called a "minor eigenvector" for convenience. In this case, the number of major eigenvectors corresponds to the number of persons moving. For example, in a situation where N persons exist, N major eigenvectors can be obtained.

이고 다른 하나는 노이즈 공간(noise space)의 행렬

이다. 신호 공간 행렬

은 신호 고유벡터(signal eigenvector)로 스팬(span)한 것이고, 노이즈 공간 행렬

은 노이즈 고유벡터(noise eigenvector)로 스팬한 것이다. 이상의 과정에서 획득한 고유벡터들 중에서 메이저 고유벡터는 신호 고유벡터에 해당하고 마이너 고유벡터는 노이즈 고유벡터에 해당하다.The eigenvector matrix constructed from the eigenvectors obtained above, It can be recognized that the subspace consists of two subspaces. One is the matrix of the signal space

And the other is the matrix of the noise space

to be. Signal space matrix

Is spanned by a signal eigenvector, and the noise space matrix

Is spanned by a noise eigenvector. Among the eigenvectors obtained in the above procedure, the major eigenvector corresponds to the signal eigenvector and the minor eigenvector corresponds to the noise eigenvector.

에 대한 AoA 값, 즉

값을 구한다. 즉, 고유벡터 행렬

을 노이즈 공간의 모든 방향 θ에 대해 프로젝션(projection)하고 역수를 취하는 것인데, 이는 다음의 [수학식 3]와 같다. 고유벡터 행렬

중에서 노이즈 공간 행렬

에 대해서만 연산을 수행하는 것이다. 이러한 행렬 연산 과정을 통해 움직이는 사람의 위치 이동 방향에 해당하는 AoA 값을 무선랜 신호로부터 구할 수 있다.
In order to track the position of a moving person, an eigenvector matrix

The AoA value for

Value. That is, the eigenvector matrix

Is projected to all directions &thetas; of the noise space and takes a reciprocal, which is shown in the following equation (3). Eigenvector matrix

The noise space matrix

To perform the operation only on the " The AoA value corresponding to the moving direction of the moving person can be obtained from the wireless LAN signal through the matrix calculation process.

Here, K is the number of noise eigenvectors.

은 움직이는 사람에 의해 반사되어 도달한 무선랜 신호의 도래각을 나타내는 값이므로,

을 이용해서 움직이는 사람의 위치를 추적할 수 있다. 만약

값이 양수이면 움직이는 사람이 무선랜 수신기(100)와 가까워지는 것이고, 음수이면 무선랜 수신기(100)와 멀어지는 것이고, 0 근처에 머물러 있으면 움직이지 않는 것이다. 따라서 시간 경과에 따른 AoA를 분석하면 시간에 따른 사람의 위치를 추적할 수 있다. 한편, 본 명세서에서 위치를 추적한다는 것은 정확한 위치를 식별해낸다는 것은 아니고 위치 변동의 추이를 식별한다는 의미이다.Arrival angle

Is a value indicating the arrival angle of the wireless LAN signal that is reflected by the moving person,

Can be used to track the location of a moving person. if

If the value is positive, the moving person is close to the wireless LAN receiver 100. If the value is negative, the moving person is distant from the wireless LAN receiver 100, and if the value is near zero, the moving person does not move. Therefore, by analyzing the AoA over time, the position of a person can be tracked over time. On the other hand, in the present specification, tracking the location means not to identify the exact position but to identify the change of the position variation.

After tracking the position of people through the above process, motion recognition is performed.

A person who intends to perform a specific operation in the present invention must first inform his / her intention to the wireless LAN receiver 100 through an operation corresponding to a predetermined preamble. Therefore, it is preferable that the wireless LAN receiver 100 normally determines only the presence or absence of a predetermined preamble operation signal and performs an operation recognition operation only when the preamble is present.

A process of determining whether or not the preamble operation signal is present will be described below.

For example, a person performs a predetermined preamble operation by repeating an operation of going backward a few steps toward the wireless LAN receiver 100. Analysis of the AoA of the wireless LAN signal caused by this operation shows that the AoA value is generated in a pattern in which a positive value and a negative value are repeated over time, that is, a pattern corresponding to the preamble operation. The WLAN receiver 100 applies a matched filter to this signal, which can be divided into extracting a positive value and extracting a negative value. It is possible to determine the presence or absence of preamble operation with respect to the wireless LAN received signal by using the match filter. The person performing the preamble operation in this manner is referred to as a 'target' in this specification.

If it is determined that there is a preamble operation, the next process is a synchronization process between the person (target) performing the operation and the WLAN receiver 100. Only when the synchronization is performed, the wireless LAN receiver 100 can recognize only the operation performed by the target and analyze the signal.

Since the WLAN signal is a signal that is reflected by several people, the signal reflected by other people acts as interference in observing the target. Accordingly, the WLAN receiver 100 performs a synchronization process with the target so as to cancel a signal acting as an interference. The process of synchronization is as follows.

First, a channel that maximizes the Doppler energy between the target and the wireless LAN receiver 100 is searched. And uses the searched channel to cancel the signal component reflected and received by another person acting as an interference among the wireless LAN signals (i.e., not performing preamble operation). Then, synchronization is established between the wireless LAN receiver 100 and the target by projecting the entire wireless LAN signal in the direction of the received signal reflected by the target.

를 최대화하는 최적 복소 가중치(complex weights)

를 찾는 것이다.
The synchronization process will be described again formally. The goal of the synchronization process is to determine the Doppler energy of the jth segment defined by < RTI ID = 0.0 > (4)

Lt; RTI ID = 0.0 > complex weights < / RTI >

.

이고,

는 i 번째 안테나의 j 번째 세그먼트에 대한 도플러 에너지를 나타낸다. 전술한 바와 같이

는 j 번째 세그먼트에 대한 도플러 에너지를 나타낸다.At this time,

ego,

Represents the Doppler energy for the j < th > segment of the i < th > As described above

Represents the Doppler energy for the j < th > segment.

Then, consider the magnitude and phase to find the optimal weight. At this time, the size to be considered is determined by the antenna gain and the phase considered is 0 to 2π. Considering the size and phase, the complexity increases considerably. To reduce the complexity, the weight of the first antenna is obtained, and the weights of the remaining antennas are taken into consideration in terms of pair-wise.

의 값을 구하고 나서, 이들을 결합하여 최적 복소 가중치 행렬

를 다음의 [수학식 5]에 의해 얻는다.
Through this process, a set of optimal complex weights

And then combines them to obtain an optimal complex weighting matrix

Is obtained by the following equation (5).

를 무선랜 신호에 적용하면 프리앰블 동작을 수행한 사람(타겟)과 무선랜 수신기(100) 간에 동기화가 맺어진다. 이를 수학적으로 표현하면, 무선랜 수신기(100)이 수신한 무선랜 신호

및 여기에 최적 복소 가중치 행렬

를 적용한 결과신호

를 나타내면 다음의 [수학식 6]과 같다.
The optimal complex weight matrix

Is applied to the wireless LAN signal, synchronization is established between the person (target) that has performed the preamble operation and the wireless LAN receiver 100. Mathematically, the WLAN signal received by the WLAN receiver 100,

And an optimal complex weighting matrix

As a result,

Is expressed by the following equation (6).

를 무선랜 신호

에 적용한다는 것은 개념적으로는 무선랜 신호

를 타겟의 방향으로 프로젝션 하는 것을 의미한다. [도 4]에 도시된 바와 같이, 이처럼 타겟([도 4]에서 사람 1)의 방향으로 무선랜 신호를 프로젝션 함으로써 다른 사람([도 4]에서 사람 2, 3)에 의해 유발된 신호는 간섭신호로 취급되어 제거되고 타겟에 의해 유발된 신호([도 4]에서 굵은 실선으로 표시)만 남게 된다. 따라서 무선랜 수신기(100)와 타겟 간에 동기화가 맺어진다.In Equation (6) above, the optimal complex weighting matrix

Lt; / RTI >

The concept of a wireless LAN signal

In the direction of the target. As shown in Fig. 4, by projecting the WLAN signal in the direction of the target (person 1 in Fig. 4), signals caused by others (persons 2 and 3 in Fig. 4) Signal and is removed and the signal caused by the target (indicated by a thick solid line in Fig. 4) remains. Thus, synchronization is established between the wireless LAN receiver 100 and the target.

을 FFT 연산하고 그 FFT 결과로부터 도플러 쉬프트를 분석하여 타겟의 동작을 인식하게 된다.
Then, the WLAN receiver 100 analyzes the operation of the target. When a person performs an operation, a Doppler shift occurs, thereby analyzing the Doppler shift to determine what the target has done. Mathematically, only the previously derived target signal

And the Doppler shift is analyzed from the result of the FFT to recognize the operation of the target.

On the other hand, in the case of the wireless LAN signal, the Doppler shift due to human operation with a bandwidth of 20 MHz is within a few hertz (Hz). In a general wireless LAN receiving circuit, it is impossible to recognize a human motion because it can not recognize a Doppler shift of several hertz generated at 20 MHz.

Accordingly, the present invention provides a technical configuration suitable for confirming the Doppler shift having a resolution in the range of several hertz. The basic idea of the present invention is to change the received wireless LAN signal into a narrow band pulse having a bandwidth of several hertz after the wireless LAN receiver 100 receives the wireless LAN signal. Since the wireless LAN signal is changed to a pulse having a bandwidth within a few hertz, the Doppler shift caused by the human action can be recognized.

Hereinafter, a process of changing a wireless LAN signal into a pulse having a narrow band will be described below. The WLAN receiver 100 converts the received time-base OFDM signal into a frequency-domain OFDM signal by performing an FFT (Fast Fourier Transform) operation. According to the OFDM technique, the frequency-domain OFDM signal after the FFT operation is expressed by Equation (7).

은 n번째 서브캐리어에 실려있는 심볼이고,

는 시간 k에 대한 시간축 OFDM 신호이고,

은 서브캐리어 개수이다.At this time,

Is a symbol carried on the n-th subcarrier,

Is the time domain OFDM signal for time k,

Is the number of subcarriers.

Here, the frequency axis OFDM signal has a bandwidth of 20 MHz. The WLAN receiver 100 changes the generated frequency-domain OFDM signal into a narrow band pulse through the following process.

First, an FFT operation is performed on the first OFDM signal received by the WLAN receiver 100, and then the signals on each subcarrier are decoded to obtain a data value. The WLAN receiver 100 knows a plurality of bits constituting data of the first OFDM signal by decoding the first OFDM signal. And uses these bits to change the M OFDM signals received thereafter to the first OFDM signal.

는 i 번째 OFDM 신호의 n번째 서브캐리어에 실린 심볼이라 정의한다. 본 발명에 따른 무선랜 수신기(100)는 i 번째 OFDM 신호를 첫 번째 심볼로 등화한다. 즉, i 번째 OFDM 신호의 n번째 서브캐리어에 실린 신호에

를 곱함으로써 i 번째 OFDM 신호를 첫 번째 OFDM 신호로 변경한다. 이러한 과정을 M 번 반복한다. 이를 통해, 무선랜 신호에서 M개의 OFDM 신호를 모두 첫 번째 OFDM 신호로 변경한 다음 IFFT (Inverse FFT) 연산을 통해 시간축 신호로 변경한다. 그 뒤 [수학식 7]에 따라 FFT 연산을 다시 수행한다. Aspect

Is defined as a symbol on the n-th subcarrier of the i-th OFDM signal. The WLAN receiver 100 according to the present invention equals the i-th OFDM signal to the first symbol. That is, the signal on the n-th subcarrier of the i-th OFDM signal

To transform the i < th > OFDM signal into the first OFDM signal. This process is repeated M times. Accordingly, all the M OFDM signals in the wireless LAN signal are changed to the first OFDM signal, and then the signals are converted into time-base signals by an inverse FFT (IFFT) operation. Then, the FFT operation is performed again according to Equation (7).

In the conventional wireless LAN technology, an FFT operation is performed for each OFDM signal. On the other hand, the WLAN receiver 100 of the present invention performs FFT operation on M consecutive OFDM signals at a time, thereby reducing the bandwidth of each OFDM subchannel by M. For example, if 2N-point FFT operations are performed on two identical OFDM signals, Equation (8) is obtained.

이기 때문에 위의 [수학식 8]을 다시 정리하면 다음의 [수학식 9]와 같다.
here

(8) is rearranged as the following equation (9).

이고 n의 값이 홀수일 때에는

이다. 따라서 위의 [수학식 9]를 다시 정리하면 다음의 [수학식 10]과 같다.
When the value of n is an even number

And the value of n is odd

to be. Therefore, the above expression (9) is rewritten as the following expression (10).

은 정수이다. 따라서 홀수 번째의 서브캐리어는 0 이고 짝수 번째의 서브캐리어는 크기가 2배가 되고 대역폭은 절반이 된다. OFDM 신호의 각 서브캐리어들이 동일한 심볼을 가지기 때문이다. 따라서 각 서브캐리어 들이 사용하는 대역폭은 반으로 줄어든다. At this time,

Is an integer. Therefore, the odd-numbered subcarriers are 0, the even-numbered subcarriers are doubled in size, and the bandwidth is halved. Since each subcarrier of the OFDM signal has the same symbol. Therefore, the bandwidth used by each subcarrier is reduced by half.

If the WLAN receiver 100 performs an MN-point FFT operation on the same M OFDM signals, the bandwidth of each subcarrier is reduced by M times. Therefore, the WLAN receiver 100 of the present invention can reduce the bandwidth of each subcarrier by performing FFT operations on the same OFDM signals at a time. As each subcarrier has a narrow bandwidth of several Hertz, it becomes possible to recognize a Doppler shift within several hertz generated in a wireless LAN signal by a human operation.

An environment has been created that can recognize Doppler shifts that occur according to human motion. The signal from this environment is analyzed, that is, the Doppler shift is analyzed to determine what kind of operation the person performed. The operations are defined in several forms in advance and the pattern of Doppler shift that occurs for each case is stored in advance. Accordingly, the Doppler shift of the wireless LAN signal is analyzed and compared with the Doppler shift pattern prepared in advance, thereby identifying whether or not the predefined operation has been performed.

For example, it can be largely defined as three operations. It is an operation in which a person extends his / her hand in the direction toward the wireless LAN receiver 100 or in a direction opposite to the wireless LAN receiver 100, or alternately with both hands. The Doppler shift for these three operations is a positive value, a negative value, and a positive value and a negative value together. Therefore, if the Doppler shift is positive, it can be judged that the hand stretching is performed. If the Doppler shift is negative, it can be judged that the hand pulling operation is performed. If the positive value and the negative value are repeated at the same time, It can be judged that an operation of extending is performed alternately. By combining these operations, new operations can be defined, and the Doppler shift of the WLAN signal can be analyzed and mapped to one of the defined operations, thereby enabling human motion recognition.

Based on the above results, it is confirmed that the location tracking and motion recognition can be performed using the wireless LAN signal. Since the signal used in this process is a wireless LAN signal, the propagation conditions are not strict so that the channel between the person and the wireless LAN receiver 100 is a line-of-sight (LOS), a non-line- sight, and in any case with a wall in the middle.

The present invention can also be embodied in the form of computer readable code on a computer readable recording medium. At this time, the computer-readable recording medium includes all kinds of recording apparatuses in which data that can be read by a computer system is stored.

Examples of the computer-readable recording medium include a ROM, a RAM, a CD-ROM, a magnetic tape, a floppy disk, an optical data storage, and the like, and may be implemented in the form of a carrier wave . The computer-readable recording medium can also be stored and executed by a computer-readable code in a distributed manner on a networked computer system. And functional programs, codes, and code segments for implementing the present invention can be easily deduced by programmers skilled in the art to which the present invention belongs.

As described above, the embodiments of the present invention have been disclosed in the present specification and drawings, and although specific terms have been used, they have been used only in a general sense to easily describe the technical contents of the present invention and to facilitate understanding of the invention. And is not intended to limit the scope of the invention. It will be apparent to those skilled in the art that other modifications based on the technical idea of the present invention are possible in addition to the embodiments disclosed herein.

100: Wireless LAN receiver
110: AoA (AoA) measuring unit
120:
130: preamble identification unit
140: Synchronization setting section
150: FFT operation unit
160: Doppler shift analysis unit
170: Shift pattern storage unit
180:

Claims (6)

A first step of tracking the position of the moving object by measuring the arrival angle AoA of the wireless LAN signal;
A second step of identifying a preamble operation preset through the generation pattern of the arrival angle;
A third step of setting a synchronization between a target object performing the preamble operation and a wireless LAN receiver by searching for a channel maximizing Doppler energy of the wireless LAN signal;
A fourth step of performing an FFT operation on the wireless LAN signal with the synchronization established;
A fifth step of analyzing a Doppler shift for the WLAN signal based on the result of the FFT operation;
A sixth step of recognizing an operation of the target object according to the Doppler shift analysis result;
Wherein the location information includes at least one of location information and location information.
The method according to claim 1,
In the first step,
Obtaining a channel matrix for the WLAN signal;
Obtaining a correlation coefficient matrix for the channel matrix;
Performing eigen decomposition on the correlation coefficient matrix to obtain one or more eigenvectors and setting an eigenvector matrix for the correlation matrix using the obtained eigenvectors;
Distinguishing a major eigenvector having a eigenvalue equal to or greater than a preset threshold value from the eigenvectors and a minor eigenvector having eigenvalues equal to or less than the threshold;
Identifying a noise space matrix constructed by spanning the eigenvector matrix with the minor eigenvectors;
Computing an angle of incidence of the wireless LAN signal reflected by the moving object by projecting and taking a reciprocal of the noise space matrix;
Analyzing the arrival angle over time to track the position of the moving object;
The method comprising the steps of: detecting a position of a wireless LAN;
The method of claim 2,
The channel matrix

The

Lt; / RTI >
The correlation coefficient matrix

silver

Lt; / RTI >
The incoming angle

The

And the location information of the mobile terminal is calculated by using the wireless LAN.
At this time,

The number of WLAN receive antennas;

Channel of the m-th user signal received by the p-th WLAN receiving antenna at the n-th sample of the wireless LAN signal;

The channel matrix

The Hermite matrix of;

Is an expectation operator; [theta] is an angle indicating the direction to the noise space; K is the number of noise eigenvectors;

Is a noise space matrix.
The method of claim 3,
In the third step,
Obtaining an optimal complex weighting matrix for maximizing Doppler energy of a wireless LAN signal;
Projecting the WLAN signal in the direction of the target object by applying the obtained optimal complex weighting matrix to the WLAN signal;
The method comprising the steps of: detecting a position of a wireless LAN;
The method of claim 4,
The fourth step is to reduce the bandwidth of the OFDM subchannel of the WLAN signal by performing an MN-point FFT operation (where M is a predetermined integer and N is the number of subcarriers) for the WLAN signal for which synchronization has been established Wherein the location information includes at least one of location information and location information.
A computer-readable recording medium having recorded thereon a program for causing a computer to execute a position tracking and motion recognition method using a wireless LAN according to any one of claims 1 to 5.

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