WO2018133264A1 - Indoor automatic human body positioning detection method and system - Google Patents

Abstract

An indoor automatic human body positioning detection method and system. The method comprises the following steps: S1: a wireless receiving end receiving a wireless signal transmitted by a wireless signal transmitter, and uploading wireless signal information to a server; S2: the server receiving the wireless signal information from the wireless receiving end, and assessing channel state information; S3: the server detecting human body positioning information according to a change in the channel state information; and S4: the server returning a calculated positioning result to the wireless receiving end. The method also comprises a parameter correction step A: using a built-in sensor for a human body to correct the parameters used in a detection algorithm in step S3. The method and system can perform accurate poisoning on a human body indoors and can use a self-learning function of a system to process a false alarm condition, thereby further reducing the false alarm rate, and can be used in any environment where a wireless network is found, and a detected person does not need to carry any additional sensing device, thus being very convenient.

Description

Method and system for automatically detecting indoor positioning of human body Technical field

The present invention relates to positioning technology, and in particular to a method for automatically detecting indoor positioning of a human body, and to a system for implementing the method.

Background technique

In recent years, indoor location information has played an increasingly important role in people's daily life. The positioning service market has developed rapidly and the demand for location services has grown rapidly. Precise positioning is very important for public safety, commercial applications, and military applications. However, the indoor environment is very complicated, and the signal transmission will be blocked by obstacles such as walls, partitions, ceilings, etc., causing signals to reflect, refract, and diffract. The transmitted signal passes through multiple paths and arrives at the receiving end at different times. Propagation phenomena and non-line-of-sight effects make indoor positioning extremely challenging. Therefore, we urgently need to find a way to easily and effectively locate the human body. For the accurate detection of the position, a method of detecting using vision, visible light or sound has been proposed. However, the detection system built by these methods has its own shortcomings. In the visual positioning detection system, it is necessary to mount a high-resolution camera in a detection environment in which the subject is located to take a large number of images, and then to track the positioning by the captured images. However, installing the camera will in some way infringe on the personal privacy of the subject. At the same time, due to the influence of light factors, the visual positioning detection system cannot work effectively in dark conditions. These systems all have shortcomings that are greatly affected by environmental factors, and the limitations of these systems are obstacles to the accurate and convenient detection of human body positioning. In systems where visible light is used for positioning, specific equipment instruments need to be installed in the environment in which the subject is located. The transmitted light cannot be blocked, otherwise there is basically no communication penetration capability. The wavelength should not be too short, otherwise it will be greatly affected by scattering, reflection, and multipath, which makes the positioning accuracy low. In systems where sound is used for positioning, other sounds or other environmental factors around the subject can easily interfere with the operation of the environmental device, resulting in lower accuracy. These systems all have shortcomings that are greatly affected by environmental factors, and the limitations of these systems have created obstacles for accurate indoor positioning detection.

With the development of wireless communication technology, more and more wireless devices are being applied to life. Therefore, the use of wireless communication technology to locate the human body is considered to be an effective and feasible method. The existing wireless communication technology proposes a variety of methods for indoor positioning of the human body. For example, through Bluetooth technology to achieve indoor positioning, the method has a high accuracy, but with this method, the testee needs to carry additional tags, which still causes inconvenience. UWB (Ultra Wideband, carrierless communication technology) pulse radio technology for indoor positioning, although the average transmission power is very low, can be "quiet coexistence" with other wireless communication systems, low energy consumption, low cost, good confidentiality, anti- Multipath interference and other advantages, but at the same time, the pulse UWB system has low spectrum utilization and is not suitable for high data rate transmission. Another important reason is UWB positioning. It is necessary to be additionally tagged by people and objects (relative to the natural label of the terminal such as a mobile phone), and these methods will cause a certain degree of inconvenience to the life of the subject.

Summary of the invention

In order to solve the problems in the prior art, the present invention provides a method for automatically detecting indoor positioning of a human body, and a system for realizing the automatic detection method for indoor positioning of the human body.

The method for automatically detecting indoor positioning of a human body comprises the following steps:

S1: the wireless receiving end receives the wireless signal transmitted by the wireless signal transmitter, and uploads the wireless signal information to the server; S2: the server receives the wireless signal information of the wireless receiving end, and evaluates the channel state information; S3: the server according to the channel state information The change detects the positioning information of the human body; S4: the server returns the calculated positioning result to the wireless receiving end.

The present invention is further improved, and further includes a parameter correction step A: correcting the parameters used in the algorithm detected in step S3 by using a sensor attached to the human body.

The present invention is further improved. The sensor includes a sensor attached to the mobile phone, and the sensor included in the mobile phone includes a gravity sensor, an acceleration sensor, and a gyroscope.

The invention is further improved, and the parameter correction method comprises:

A1: setting the start and end of the human body motion, recording the motion characteristics with the sensor, and recording the time period;

A2: The sensor calculates the number of steps of human motion during this time period;

A3: The speed of the human body is calculated by the time period and the number of steps, and the parameters of the detection algorithm are corrected.

The present invention is further improved. In step S2, evaluating channel state information includes the following steps:

S21: Collect channel state data, where the channel state data includes CSI values of M subcarriers in N spatial streams, where N and M are both natural numbers greater than one;

S22: For each spatial stream, obtain an average value of CSI values of P consecutive subcarriers at the same time point, and use the average value as channel state information, and P is a natural number greater than 1 and less than M;

S23: Smoothing channel state information.

The present invention is further improved. The processing method of step S3 includes the following steps:

S31: based on the statistical learning theory, pre-establishing an indoor propagation model in which the channel state information CSI and the distance are used as training samples in the set space;

S32: receiving wireless signal transmitter coordinates in the network layer and channel state information CSI from the physical layer;

S33: Applying the trilateral positioning method and the indoor propagation model to complete the positioning of the human body.

The present invention is further improved. The processing method of step S3 further includes step S34: adaptively modifying parameters in the positioning algorithm in step S33 by using a fingerprint card algorithm.

The present invention is further improved. The processing method of step S3 further includes step S35: establishing a database, and positioning the bit Set the sample to map to the radio map.

The present invention is further improved. In step S35, a statistical machine learning is used to create a determination region for the channel response mode, and the received wireless signals are cross-correlated for location identification. The method for the statistical machine to learn to identify the location includes the following steps:

B1: calculating a cross-correlation of channel response information of the received wireless signal at the sampling point;

B2: statistics the cross-correlation of the received wireless signals of the overall statistical information of the channel responses at each grid point;

B3: calculating the number of correlations between the antennas;

B4: The Mahalanobis distance is calculated according to the multidimensional Gaussian distribution and the maximum likelihood estimation method, and the human body position is determined by the best match with the radio signal in the radio map in the indoor propagation model.

The present invention also provides a system for implementing the method, comprising: a wireless signal transmitter for transmitting a wireless signal; and a wireless receiving end for receiving a wireless signal transmitted by the wireless signal transmitter and uploading the wireless signal information to the server, Obtaining the positioning result returned by the server; the server is configured to receive the wireless signal information of the wireless receiving end, and evaluate the channel state information, detect the positioning information of the human body according to the change of the channel state information, and then return the calculated positioning result to the wireless receiving end.

Compared with the prior art, the invention has the beneficial effects that the detection accuracy of the detected action is 84% to 94% in an indoor environment with less decoration, and in an indoor environment with more decorations, the detection is performed. Accuracy rate can reach 78%; can accurately locate the human body indoors, and use the system's self-learning function to deal with false positives, further reduce the false positive rate; based on the existing wireless network and equipment, carry out indoor Inspection work, no need to install other specific detection equipment in the tested environment, can be used in any environment spread over the wireless network, has a very high popularity, and the testee does not need to carry any additional sensing equipment, avoiding being detected The inconvenience caused by carrying the testing equipment provides convenience for their lives.

DRAWINGS

1 is a schematic structural diagram of a system according to an embodiment of the present invention;

2 is a schematic diagram of a server implementation process of the present invention;

3 is a flow chart of a method according to an embodiment of the present invention.

detailed description

The present invention will be further described in detail below with reference to the accompanying drawings and embodiments.

As shown in FIG. 1 , the automatic indoor positioning automatic detection system of the present invention comprises a wireless signal transmitter for transmitting a wireless signal, and a wireless receiving end for receiving a wireless signal transmitted by the wireless signal transmitter and uploading the wireless signal information to the server. Obtaining the positioning result returned by the server; the server: receiving the wireless signal information of the wireless receiving end, and evaluating the channel state information, detecting the positioning information of the human body according to the change of the channel state information, and then positioning the calculation The result is returned to the wireless receiver.

In a practical application, preferably, the wireless receiving end is a wireless network card, the wireless signal transmitter is a wireless router, and the server is a mobile phone, and the method is based on a radio propagation mechanism in an indoor environment, and establishes a wireless signal and a human body movement. The relationship between the distances only needs to use the existing wireless network equipment of the home, that is, the change of the wireless signal caused by the distance of the detected person can be analyzed, thereby obtaining a precise indoor positioning position. In a specific indoor environment, the rich channel state information of the wireless network can be collected through the wireless network card. In the present invention, the system has a plurality of antennas for transmitting and receiving wireless signals, respectively; the wireless network card used by the system can receive channel state information. The number of the wireless signal transmitters is one or two, and the number of the wireless receiving ends is one or more. Preferably, the number of wireless signal transmitters and wireless receiving ends is 2 or 3 respectively. As shown in FIG. 1, there are two wireless signal transmitters (a first wireless signal transmitter and a second wireless signal transmitter) and two wireless receiving ends (a first wireless receiving end and a second wireless receiving end) in the detected environment. ). The first wireless receiving end receives the CSI from the first wireless signal transmitter (the abbreviation of Channel State Information, that is, channel state information, in the field of wireless communication, CSI is the channel attribute of the communication link, and describes the signal in each transmission path. The weakening factor), the second wireless receiving end receives the CSI from the second wireless signal transmitter. In the environment being tested, the subject does not need to carry other additional equipment. The system will use the CSI received by the two wireless receiving ends to detect the motion of the detected object, and thereby determine the motion or position of the detected object, such as whether it falls or the like.

In order to establish a connection between the wireless signal and the human body moving distance, the present invention uses the channel state information CSI of the wireless network as an indicator. CSI can describe the propagation path of a signal under the combined influence of time delay, amplitude attenuation, and phase shift. Based on the radio propagation model in an indoor environment, the present invention establishes a link between CSI and human body travel distance. In a particular indoor environment, such as a room, there is a main propagation path and multiple reflection paths due to the influence of the surrounding environment, such as ceilings, floors, and walls. When the subject is in the room, his body will generate multiple scattering paths; when the subject remains stationary in the environment, the receiving end in the environment will receive stable propagation energy; When moving to another location, the scattering point that is affected by the human body and produces a scattering path will quickly change position, and this mutation will cause the energy received at the receiving end to change. With this change, the present invention will determine the change in the distance traveled by the human body. The present invention uses Orthogonal Frequency Division Multiplex (OFDM) to obtain CSI in the form of a subcarrier. The connection between the CSI obtained by this method and the human body movement will improve the accuracy of the judgment of the action.

As shown in FIG. 2 and FIG. 3, the method for automatically detecting the indoor positioning of the human body based on the above system includes the following steps:

S1: The wireless receiving end receives the wireless signal transmitted by the wireless signal transmitter, and uploads the wireless signal information to the server. When the system of the present invention starts to work, the wireless signal transmitter will propagate the wireless network signal, and the wireless receiving end (such as the mobile terminal) in a specific area collects the CSI as the initial channel state data and uploads it to the server. After the data processing through the server.

S2: The server receives the wireless signal information of the wireless receiving end, and evaluates the channel state information, where the evaluating the channel state information includes the following steps:

S21: Collect channel state data, where the channel state data includes CSI values of M subcarriers in N spatial streams, where N and M are both natural numbers greater than one;

S22: For each spatial stream, obtain an average value of CSI values of P consecutive subcarriers at the same time point, and use the average value as channel state information, and P is a natural number greater than 1 and less than M;

S23: Smoothing channel state information.

Specifically, the present invention uses a 3×3 Multiple-Input Multiple-Out-put (MIMO) as an example. The initial channel state data obtained during the sensing phase is divided into 9 spatial streams, and each There will be 30 subcarriers in a stream. Changes in the distance traveled by the human body will affect the data contained in different spatial streams, and will have a similar effect on all subcarriers in each spatial stream. At the same time, experiments have shown that environmental factors (such as temperature, room settings) can also cause fluctuations in the collected CSI. Therefore, in the present invention, the CSI values of 30 subcarriers in each independent spatial stream are combined and combined into a single channel state information. Preferably, for each spatial stream, the CSI average of five consecutive subcarriers is first obtained, and the average of the CSIs of the same time point is taken as the channel state information for each of the nine spatial streams. In order to reduce the interference of environmental factors, the present invention utilizes a data filtering technique and a moving average method, specifically, using a weighted moving average to smooth the channel state information processed by the above to reduce noise in the data. .

S3: The server detects the positioning information of the human body according to the change of the channel state information. The processing method for detecting human body positioning information includes the following steps:

S31: based on the statistical learning theory, pre-establishing an indoor propagation model in which the channel state information CSI and the distance are used as training samples in the set space;

S32: receiving wireless signal transmitter coordinates in the network layer and channel state information CSI from the physical layer;

S33: Applying the trilateral positioning method and the indoor propagation model to complete the positioning of the human body.

The processing method of step S3 further includes step S34: adaptively modifying parameters in the positioning algorithm in step S33 by using a fingerprint card algorithm. In order to achieve precise positioning with a small amount of training samples.

According to the existing radio propagation model, the present invention develops an indoor propagation model based on CSI information and distance. Through a simple supervised learning-based rapid training method, and through the three-sided positioning method for positioning. The first step is to collect multiple grouped CSI data at two points to train the environmental factor and the path loss fading index of the indoor propagation model; the second step: test the efficiency of the parameter estimation using the CSI collected at the third point. Through the above method, an indoor propagation model based on CSI information and distance is established, and the distance between the unknown point and the known point can be calculated by the CSI information. The unknown point must be located on a sphere with a known radius as the center of the sphere. As long as the unknown point and the three known points are measured For the distance, the unknown point is at the intersection of the three sphere circumferences. When there are two points at the intersection, because there is a receiving direction, a point on the back side of the reception can be discarded, so that the position of the unknown point can be accurately measured.

In order to improve accuracy and reduce training samples, the present invention uses a fingerprint method to correct the positioning algorithm. Beacon information from a wireless signal transmitter is received at each sample point location, the beacon information including channel response information for a plurality of subcarriers. The mobile receiver receives 30 sets of CSI information at the same time.

Furthermore, the processing method of step S3 of this example further includes step S35: establishing a database to map the located location samples to the radio map.

In step S35, this example uses statistical machine learning to create a decision region for the channel response mode and cross-correlate the received wireless signals for the mode of position recognition.

As shown in Figure 4, the algorithm is located using a maximum likelihood estimate with some probability distribution assumptions. Through the central limit theorem, it is actually assumed that the eigenvector follows a multidimensional Gaussian distribution characterized by two statistical parameters (ie, mean vector and covariance matrix). Using the multidimensional Gaussian distribution and its covariance matrix, the decision region can be defined by the Mahalanobis distance. The machine learning phase is equivalent to calculating the average vector and covariance matrix of the sample data during statistical machine learning.

Specifically, the method for implementing the statistical machine learning to identify the location is as follows:

First, the cross-correlation of the channel response information of the received wireless signal at the sampling point is calculated. The relevant statistical properties can be described by the statistical properties of the autocorrelation function of the transmitted signal and the cross-correlation function of the channel response.

表示第i根天线在位置ψ的接收信号，

和

分别表示信道响应和发射信号，

表示为零均值高斯噪声，1≤m≤M,1≤τ≤m,

为动作起始和结束之间的时间段。among them,

Indicates the received signal of the ith antenna at position ,,

with

Representing the channel response and the transmitted signal, respectively.

Indicates zero mean Gaussian noise, 1 ≤ m ≤ M, 1 ≤ τ ≤ m,

The time period between the start and end of the action.

The statistical properties of the autocorrelation function of the transmitted signal can be considered to be constant as it is determined by the modulation and transmission filters of the signal. On the other hand, the cross-correlation function of the channel response depends on the changing environment and the location of the transmitter. Therefore, the cross-correlation fluctuations are mainly affected by the cross-correlation of the channel response. This example uses statistical machine learning, where fluctuations are used as statistical properties of the channel response. This means that the proposed method needs to statistically implement cross-correlation of received signals with overall statistical information about the channel response at each grid point, as follows:

具有2N+1个维度，其中N由最大时延扩展确定。由于手机端自带两根距离很相近的天线，所以k和l可看成一个相同的传感器。所以本例互相关系数的学习数据库可以定义为：In this example, 2N+1 cross-correlated discrete samples are acquired, ie -N≤n≤N. therefore,

There are 2N+1 dimensions, where N is determined by the maximum delay spread. Since the mobile phone comes with two antennas with similar distances, k and l can be regarded as one and the same sensor. So the learning database of the cross-correlation of this example can be defined as:

遵循由两个统计参数(即平均值向量和协方差矩阵)为表征的多维高斯分布。利用多维高斯分布及其协方差矩阵，决定区域可以由马氏距离来定义。机器学习阶段等效于在统计机器学习过程中计算样本数据的平均向量和协方差矩阵。本例假设特征向量

遵循多维高斯分布，以及可以通过采用最大似然估计法来计算马氏距离。然后，如下定义学习数据，即平均值向量和协方差矩阵：This algorithm is improved by using maximum likelihood estimation with some probability distribution assumptions. The eigenvector is actually assumed by the central limit theorem

A multidimensional Gaussian distribution characterized by two statistical parameters (ie, mean vector and covariance matrix) is followed. Using the multidimensional Gaussian distribution and its covariance matrix, the decision region can be defined by the Mahalanobis distance. The machine learning phase is equivalent to calculating the average vector and covariance matrix of the sample data during statistical machine learning. This example assumes a eigenvector

The multidimensional Gaussian distribution is followed, and the Mahalanobis distance can be calculated by using the maximum likelihood estimation method. Then, the learning data, ie the mean vector and the covariance matrix, are defined as follows:

为

的平均向量，

为以

的

协方差矩阵，将概率函数形成决策区域定义为：Where H is the conjugate transpose of the matrix (also known as Hermitian transposition),

for

Average vector,

To

of

The covariance matrix defines the probability function forming decision area as:

p(xxx) is the intrinsic format of a probability function. In fact, p refers to the probability of the probability function of the decision region. The expression p(x|ψ) means the probability of seeking under the condition of ψ.

和协方差矩阵

都是天线k与l和位置信息ψ的函数。当无线信号发射器的位置ψ已知，即可以定义对应决策区域的评估函数。在预测位置的时候，最可疑位置可以被估计为具有最高似然性的位置，即与先前训练模型中无线电地图中的无线电信号之间的最佳匹配来推断其位置。In this method, the average vector

And covariance matrix

Both are functions of the antennas k and l and the position information ψ. When the position of the wireless signal transmitter is known, an evaluation function corresponding to the decision area can be defined. When predicting a location, the most suspicious location can be estimated as the location with the highest likelihood, ie, the best match between the radio signals in the radio map in the previous training model to infer its location.

Wherein, the present invention further includes a parameter correction step A: correcting the parameters used in the algorithm detected in step S3 by using a sensor attached to the human body.

The sensor of this example is preferably a sensor that is provided by the mobile phone, such as a gravity sensor, an acceleration sensor, a gyroscope, and the like that are provided by the mobile phone.

Specifically, the parameter correction method includes:

A1: setting the start and end of the human body motion, recording the motion characteristics with the sensor, and recording the time period;

A2: The sensor calculates the number of steps of human motion during this time period;

A3: The speed of the human body is calculated by the time period and the number of steps, and the parameters of the detection algorithm are corrected.

Taking the mobile phone sensor as an example, the shaking mobile phone action is used as a flag for setting the start and end of the movement, and the time period is recorded at the start and end time points, and the distance moved by the time period is calculated by using the mobile phone's own sensor. The distance and angle information collected are used to calculate the position after the movement, and the positioning algorithm is corrected to make the positioning more accurate.

The invention further comprises a step S4: the server returns the calculated positioning result to the wireless receiving end. After the server calculates the positioning result, the positioning result is returned to the wireless receiving end, such as a wireless network card, and other required terminals can obtain positioning information from the wireless network card.

The invention is based on the radio propagation mechanism in the indoor environment, establishes the channel state information CSI and the human body moving distance, and judges the distance of the human body movement through the change of the CSI, thereby effectively calculating the position of the human body indoor, which has the following beneficial effects: In indoor environments with less artifacts (such as laboratories), the detection accuracy of detected actions is 84% to 94%, while in indoor environments with more decorations (such as dormitory), the detection accuracy can reach 78%. . It can accurately locate the human body indoors, and use the self-learning function of the system to deal with false alarms and further reduce the false alarm rate. The present invention performs indoor testing on the basis of existing wireless networks and devices. There is no need to install other specific detection equipment in the detection environment, it can be used in any environment spread over the wireless network, and it has high popularity. At the same time, the testee does not need to carry any additional sensing equipment, and the testee is not required to carry the detection equipment. The inconvenience caused by it has facilitated its life.

The embodiments described above are preferred embodiments of the present invention, and are not intended to limit the scope of the present invention. The scope of the present invention is not limited to the specific embodiments. Within the scope of protection of the present invention.

Claims (10)

The invention discloses an automatic detection method for indoor positioning of a human body, which comprises the following steps: S1: the wireless receiving end receives the wireless signal transmitted by the wireless signal transmitter, and uploads the wireless signal information to the server; S2: the server receives the wireless signal information of the wireless receiving end, and evaluates channel state information; S3: The server detects the positioning information of the human body according to the change of the channel state information; S4: The server returns the calculated positioning result to the wireless receiving end. The method for automatically detecting the indoor positioning of a human body according to claim 1, wherein after the step S3 is executed, the positioning information is calculated, and the parameter correction step A is performed: detecting the step S3 by using the sensor attached to the human body. The parameters used in the algorithm are corrected. The method for automatically detecting a human body indoor position according to claim 2, wherein the sensor comprises a sensor attached to the mobile phone, and the sensor included in the mobile phone comprises a gravity sensor, an acceleration sensor, and a gyroscope. The method for automatically detecting a human body indoor position according to claim 3, wherein the parameter correction method comprises: A1: setting the start and end of the human body motion, recording the motion characteristics with the sensor, and recording the time period; A2: The sensor calculates the number of steps of human motion during this time period; A3: The speed of the human body is calculated by the time period and the number of steps, and the parameters of the detection algorithm are corrected. The method for automatically detecting indoor positioning of a human body according to any one of claims 1 to 4, wherein in step S2, evaluating channel state information comprises the following steps: S21: Collect channel state data, where the channel state data includes CSI values of M subcarriers in N spatial streams, where N and M are both natural numbers greater than one; S22: For each spatial stream, obtain an average value of CSI values of P consecutive subcarriers at the same time point, and use the average value as channel state information, and P is a natural number greater than 1 and less than M; S23: Smoothing channel state information. The method for automatically detecting a human body indoor position according to claim 5, wherein the processing method of step S3 comprises the following steps: S31: based on the statistical learning theory, pre-establishing an indoor propagation model in which the channel state information CSI and the distance are used as training samples in the set space; S32: Receive channel state information CSI from a physical layer of the transmitting end; S33: Applying the trilateral positioning method and the indoor propagation model to complete the positioning of the human body. The method for automatically detecting the indoor positioning of the human body according to claim 6, wherein the processing method of step S3 further comprises the step S34: adaptively modifying the parameters in the positioning algorithm in step S33 by using a fingerprint card algorithm. The method for automatically detecting a human body indoor position according to claim 7, wherein the processing method of step S3 further comprises the step S35: establishing a database to map the located position samples to the radio map. The method for automatically detecting indoor positioning of a human body according to claim 8, wherein in step S35, statistical machine learning is used to create a determination region for the channel response mode, and the received wireless signals are correlated with each other for position recognition. The method of statistical machine learning to identify a location in a radio map includes the following steps: B1: calculating a cross-correlation of channel response information of the received wireless signal at the sampling point; B2: statistics the cross-correlation of the received wireless signals of the overall statistical information of the channel responses at each grid point; B3: calculating the number of correlations between the antennas; B4: The Mahalanobis distance is calculated according to the multidimensional Gaussian distribution and the maximum likelihood estimation method, and the human body position is determined by the best match with the radio signal in the radio map in the indoor propagation model. A system for implementing an automatic detection method for a human body indoor position according to any one of claims 1-9, characterized in that it comprises a wireless signal transmitter for transmitting a wireless signal; The wireless receiving end is configured to receive a wireless signal transmitted by the wireless signal transmitter, and upload the wireless signal information to the server, and obtain a positioning result returned by the server; The server is configured to receive wireless signal information of the wireless receiving end, evaluate the channel state information, detect the positioning information of the human body according to the change of the channel state information, and then return the calculated positioning result to the wireless receiving end.

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