CN108038419B - Wi-Fi-based indoor personnel passive detection method - Google Patents

Wi-Fi-based indoor personnel passive detection method Download PDF

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CN108038419B
CN108038419B CN201711137357.4A CN201711137357A CN108038419B CN 108038419 B CN108038419 B CN 108038419B CN 201711137357 A CN201711137357 A CN 201711137357A CN 108038419 B CN108038419 B CN 108038419B
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孙力娟
谈青青
朱海
肖甫
郭剑
韩崇
周剑
王娟
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Nanjing University of Posts and Telecommunications
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Abstract

The invention discloses a Wi-Fi-based indoor personnel passive detection method. Establishing reasonable indoor measurement point distribution in an off-line stage, completing data acquisition in the off-line stage, and selecting data of a part of measurement points for off-line training; after original data are exported, denoising processing is carried out on the original data, a probability density function PDF is generated, and a threshold value delta is determined; using a sliding window and calculating a sliding window correlation coefficient matrix of each receiving antenna; calculating a correlation coefficient matrix TT of data received by a receiving antenna in an online state, drawing a PDF image of the matrix TT, comparing the maximum point of the image with a threshold value delta, judging whether a test state is a static state, and determining the specific state of a target in the current scene. The method is based on the fine-grained physical layer information CSI, the correlation among the subcarriers is calculated on the frequency domain to generate the fingerprint, the influence of the wireless signals on the time domain by environmental factors is avoided, the occurrence of false alarm can be reduced through the threshold value determined by multiple times of training and the voting mode, and the detection accuracy is high.

Description

Wi-Fi-based indoor personnel passive detection method
Technical Field
The invention belongs to the field of wireless sensor networks, and relates to a Wi-Fi (wireless fidelity) -based indoor personnel passive detection method, which aims to solve the problems that the existing passive personnel detection method is greatly influenced by environmental changes and has insufficient detection accuracy and robustness, and meanwhile, the current activity state of a target is further judged under the condition that the target is detected to exist.
Background
With the rapid development of network technology, wireless signals are spread over substantially every corner of life. Today, people are working for outdoor activities and work for a reduced time, most activities being performed indoors, such as working, eating, shopping, entertainment, etc. The passive indoor personnel detection method based on the Channel State Information (CSI) characteristics can be applied to the aspects of intelligent home, medical monitoring, safety detection and the like.
In an indoor environment, a wireless signal reaches a receiving end through a plurality of paths by reflection, diffraction, scattering, and the like, as shown in fig. 1. The wireless signals of different paths have different delays, attenuations and phase changes, and the receiving end receives signals formed by fusing the complex wireless signals. When people exist indoors, no matter a static target or a moving target affects wireless signals propagating in the space, so that whether the people exist in a scene or not can be judged by comparing the characteristic difference of the wireless signals in the unmanned state and the target state of the scene.
Indoor personnel detection can be completed by analyzing the variation difference of wireless signals in different scenes, wherein the fingerprint model method is the current mainstream technology, and has the advantages of low consumption, simpler parameter acquisition, high precision and the like. The method based on the fingerprint model comprises an off-line part and an on-line part: in an off-line state, respectively adopting wireless signals in an unmanned environment and in the presence of a target, and extracting characteristic fingerprints; and in an online state, acquiring a wireless signal, extracting a characteristic fingerprint, comparing the characteristic fingerprint with the characteristic fingerprint in an offline state, and judging whether a person is in a scene or not. However, the existing detection method usually extracts the feature fingerprint in the time domain, which is greatly influenced by the environment, has limitations and low accuracy, and these problems are all to be further improved.
Since the Received Signal Strength Indication (RSSI) can be easily obtained through a wireless technology and a cellular network, the conventional wireless sensing means uses the RSSI. However, RSSI is an energy characteristic of a Media Access Control (MAC) layer, is easily affected by multipath effect, is very unstable, and is only suitable for a scene with a simple environment and low accuracy requirement. Specifically, in a typical indoor environment, the wireless signal needs to undergo reflection, diffraction, scattering, etc. during the whole propagation process, and finally reaches the receiving end through multiple paths. The wireless signals from different paths have different delays, attenuations and phase changes, and a receiver receives a signal formed by fusing the complex wireless signals at a certain time point. RSSI is very unstable as a superposition of signals from multiple paths. Even a static link without artifacts will fluctuate at different points in time. This is because the fluctuation from a certain path, although very small, may cause the final superimposed value to change drastically after the superposition of the fluctuations of all links.
The CSI refers to channel properties of a communication link, which describe attenuation factors of a signal during propagation between a signal transmitter and a signal receiver, including scattering, environmental attenuation, distance attenuation, and other information, three of multiple paths through which the CSI propagates indoors are shown in fig. 2, and fig. 3 is a signal waveform corresponding to the three paths in fig. 2. The CSI generally includes instantaneous CSI and statistical CSI, wherein the instantaneous CSI can be regarded as an impulse response of a digital filter under the condition that the channel condition is known, and the signal transmitter and receiver can adapt to the impulse response of a transmission signal through the instantaneous CSI, so as to optimize spatial multiplexing and reduce the data transmission error rate; the statistical CSI is a statistical value of the long-term observation result of the channel, and the distribution of the signal attenuation factor, the average channel gain, the spatial correlation, and the like can be known through the statistical information. CSI is more fine-grained and stable than RSSI, but accurate acquisition requires specialized equipment, which is an obstacle in initial research. However, with the widespread use of Orthogonal Frequency Division Multiplexing (OFDM), researchers can obtain sampled versions of Channel Frequency Responses (CFRs) of CSI on different subcarrier frequencies by modifying the firmware, and therefore, recent research is turning to using finer-grained CSI information.
The existing detection method based on the CSI processes wireless signals in the time domain and extracts characteristic fingerprints, however, the wireless signals in the time domain are affected by environmental factors, so that the detection method has limitations and low accuracy.
Disclosure of Invention
The invention aims to solve the problems that the existing method can only detect the scene unmanned state and the existing dynamic target state, and the influence of environmental factors and multipath effects on the fingerprints extracted in the time domain is large. The method comprises the steps of extracting effective characteristic fingerprints on a frequency domain based on fine-grained physical layer information (CSI), training unmanned scenes, static targets and characteristic fingerprints when moving target scenes exist in an off-line mode, determining thresholds for distinguishing the unmanned and the manned by analyzing signal characteristics extracted in different states for each receiving antenna, and drawing a probability distribution histogram when the static targets and the moving targets exist; in the on-line stage, whether a detection target exists is judged firstly by calculating the signal characteristics of the sampling signals, when the detection target exists, the current probability distribution histogram is further calculated, and the current state of the detection target is judged by comparing the current probability distribution histogram with the static and dynamic histograms trained in advance. After the detection result of each receiving antenna is obtained, a final detection result is determined by using a voting scheme.
In order to achieve the purpose, the technical scheme adopted by the invention is a Wi-Fi-based indoor personnel passive detection method, which specifically comprises the following steps:
the method comprises the following steps: in the off-line stage, reasonable indoor measuring point distribution is established, a scene is divided into a plurality of areas after obstacles are removed, and data of measuring points are collected when a static target is positioned; secondly, acquiring data of random walking in a measurement area during dynamic target; finally, data when no target to be measured exists in the scene are collected, data collection in an off-line stage is completed, and data of a part of measuring points are selected for off-line training, so that training cost is reduced;
step two: deriving measured raw data;
step three: carrying out noise reduction processing on the original data, removing abnormal points through a filter, and simultaneously eliminating noise mixed in the original data due to multipath effect and environmental influence in the CSI propagation process by using median filtering;
step four: computing scenario noneCorrelation coefficient C in the frequency domain at the time of human-time and presence of targetsta、
Figure BDA0001470740410000031
And
Figure BDA0001470740410000032
generating a probability density function PDF, and determining a threshold value delta according to the intersection point of PDF images;
step five: continuously analyzing the characteristic fingerprints of static objects and dynamic objects in the scene by using a sliding window, and calculating a sliding window correlation coefficient matrix C of each receiving antenna by using the sliding windowstan_aveAnd Cdyn_aveTo draw out Cstan_aveAnd Cdyn_aveThe frequency distribution histogram of (1);
step six: and in the online stage, acquiring a group of online data with any duration, calculating a correlation coefficient matrix TT of data received by each receiving antenna in an online state, drawing a PDF image of the matrix TT, comparing the size of the highest point of the image with a threshold value delta, judging whether the test state is a static state, if the current state is a scene internal target according to the comparison result of the test values on not less than two antennas and the threshold value delta, continuously calculating a correlation coefficient matrix TT 'on a frequency domain in a sliding window on each antenna, respectively drawing probability distribution histograms of the measured data TT' in the three antenna states, comparing the probability distribution histograms with the static target and the dynamic target, and determining that the specific state with high similarity is the current scene internal target.
Preferably, the filter is a hampel filter.
Further, the fourth step specifically includes:
step 4.1: under the condition of no person in the scene, calculating the correlation coefficient between every two subcarriers, and generating a static fingerprint C by data received by each receiving antennastaComprises the following steps:
Figure BDA0001470740410000033
Figure BDA0001470740410000041
wherein the content of the first and second substances,
Figure BDA0001470740410000042
the correlation coefficient between the ith subcarrier and the (i + 1) th subcarrier in the scene unmanned state is obtained;
step 4.2: under the state of having static object, the related coefficient is calculated, the fingerprint of the static object is generated by the data received by each receiving antenna
Figure BDA0001470740410000043
Comprises the following steps:
Figure BDA0001470740410000044
Figure BDA0001470740410000045
Figure BDA0001470740410000046
wherein the content of the first and second substances,
Figure BDA0001470740410000047
for the correlation coefficient between the ith subcarrier and the (i + 1) th subcarrier in the presence of a stationary target state, Cstan_lA fingerprint vector on a measuring point, and l is a measuring position point;
step 4.3: under the condition of existence of dynamic target, calculating correlation coefficient, and generating fingerprint with dynamic target by data received by each receiving antenna
Figure BDA0001470740410000048
Comprises the following steps:
Figure BDA0001470740410000049
Figure BDA00014707404100000410
Figure BDA00014707404100000411
wherein the content of the first and second substances,
Figure BDA00014707404100000412
for the correlation coefficient between the ith subcarrier and the (i + 1) th subcarrier in the presence of a dynamic target state, Cdyn_lA fingerprint vector on a measuring point, and l is a measuring position point;
step 4.4: c is to besta、
Figure BDA00014707404100000413
And
Figure BDA00014707404100000414
the probability density function PDF is generated in a graph, and the distinguishing between the unmanned state and the manned state is obvious, and the distinguishing between the static object and the dynamic object is not obvious, so the numerical values of the highest points of the PDF images are compared, and the boundary between the manned state and the unmanned state of the scene is determined by the threshold value delta.
Further, the fifth step includes:
step 5.1: characteristic fingerprint C generated by data received by each receiving antenna when a static target existsstan_aveComprises the following steps:
Figure BDA0001470740410000051
Figure BDA0001470740410000052
wherein the content of the first and second substances,
Figure BDA0001470740410000053
averaging the characteristic fingerprints between subcarriers under each sliding window;
step 5.2: characteristic fingerprint C generated by data received by each receiving antenna when dynamic targets existdyn_aveComprises the following steps:
Figure BDA0001470740410000054
Figure BDA0001470740410000055
wherein the content of the first and second substances,
Figure BDA0001470740410000056
is the average value of the characteristic fingerprints among the subcarriers under each sliding window.
Preferably, in the fifth step, the sliding window is 5 seconds.
Also, preferably, the sliding window is set to 5 seconds in the above-described step six.
Compared with the prior art, the invention has the beneficial effects that:
(1) the equipment is irrelevant and the implementation is convenient.
The passive indoor personnel detection method based on the CSI characteristic fingerprint is equipment-independent, can complete work only by a common commercial router capable of sending wireless signals and a computer which is provided with an Intel 5300 network card and can output CSI information, does not need to add additional wireless equipment, does not need to carry related sensor equipment, and is low in cost and easy to implement.
(2) CSI has abundant signal characteristics, and has great advantages.
Compared with the fingerprint features of RSS alone, CSI has richer fingerprint information, such as frequency attenuation characteristics, phase and energy intensity, etc., which can reflect the signal features of the target intrusion and perceive finer environmental information in time domain and frequency domain.
(3) The CSI has stronger stability
Under the same environment, compared with RSS, the CSI has the characteristics of relatively stable overall structural characteristics, relatively stable stability in a static environment and sensitivity to target motion, and is more suitable for a complex indoor positioning environment. Meanwhile, the CSI can also characterize multipath propagation to a certain extent.
(4) The detection accuracy is high, and the specific state of the target can be judged
The fingerprint-based indoor personnel detection method is customized according to the scene, and the exclusive characteristic fingerprint is trained according to the applied scene. According to the scheme, effective CSI characteristics are extracted, correlation among subcarriers is calculated on a frequency domain to generate fingerprints, the influence of environmental factors on wireless signals on a time domain is well avoided, the occurrence of false alarm can be reduced through a threshold value determined through multiple times of training and a voting mode, and therefore the detection accuracy is high. In addition, the scheme not only can detect whether the target exists in the scene, but also can judge the specific state of the target, and has diversity.
Drawings
Fig. 1 is a schematic illustration of propagation of multiple paths.
Fig. 2 is a signal diagram of three paths of CSI in the indoor propagation process.
Fig. 3 is a signal waveform diagram for three different paths.
Fig. 4 is a layout diagram of a test scenario.
Fig. 5 is a flowchart of a passive indoor person detection method based on CSI feature fingerprints in an offline state.
Fig. 6 is a flowchart of a passive indoor person detection method based on CSI feature fingerprints in an online state.
Detailed Description
The present invention will now be described in further detail with reference to the accompanying drawings.
The invention relates to a passive indoor personnel detection method based on CSI (channel state information) characteristic fingerprints, wherein in a stable channel, channel information can be modeled as follows in a frequency domain:
y＝Hx+n (13)
wherein y and x are vectors of a signal receiving end and a signal transmitting end respectively, H is a channel information matrix, and n is a Gaussian noise vector. By the formula, we can obtain the CSI calculation formula of all subcarriers:
Figure BDA0001470740410000061
the CSI on each subcarrier can be expressed as:
H＝|H|ejsin(∠H) (15)
where H and H are the amplitude and phase of each subcarrier, respectively.
In an indoor scene 8.6m long and 5.7m wide, the invention uses a Wireless Access Point (AP), namely a Wireless router, and a computer provided with a 3-antenna Intel 5300 network card as a Monitoring Point (Monitoring Point, MP), and obtains CSI data by modifying firmware, wherein an experimental platform is a Wi-Fi-based Wireless sensing system platform which is built based on an integrated installation Tool TNS-CSI Tool of 'Linux802.111n CSI Tool', and specifically comprises the following implementation steps:
step 1: and establishing reasonable indoor measuring point distribution to finish data acquisition in an off-line stage.
The method of the invention is divided into an off-line stage and an on-line stage based on CSI characteristic fingerprints and a fingerprint model. The work of the off-line stage is as follows:
(1) the ground except the occupied positions of tables, chairs and the like in the scene is divided into 32 areas of 1m by 1m, and the central point of each area is set as a measuring point. The AP and the MP are placed on two sides of the scene and on the same straight line. As shown in fig. 4.
(2) The measured object is still on the measuring point, and 32 groups of data are collected when the still object exists.
(3) The measured target moves randomly in 32 areas in sequence, and 32 groups of data are collected when the moving target exists.
(4) And no target to be detected exists in the scene, and data in static state without the target is collected.
In the invention, in order to reduce the cost in the off-line training, measuring points 1, 3, 5, 7, 9, 11, 13, 15, 18, 20, 22, 24, 25, 27 and 29 are selected, the CSI values received by 15 points are used as off-line training data, and the data received by all 32 measuring points are used for detecting the accuracy of the method provided by the invention.
Step 2: deriving the raw data, in the present invention, the set of matrices H (T50 × T, packet rate 50packets/s, T time) with CSI collected per antenna being 30 × T, shows the channel gain on different subcarriers from the transmitting antenna to the receiving antenna. The CSI matrix H received by each antenna at one measurement point is:
Figure BDA0001470740410000071
wherein h isiCSI vector representing the ith subcarrier:
Figure BDA0001470740410000072
and step 3: in wireless communication, the CSI is affected by multipath effects and environment during propagation, and the raw data is inevitably mixed with a certain degree of noise. Here, a hampel filter is used to remove outliers:
H′＝hampel(H,50*k) (18)
wherein, H is the initial CSI measured at the measuring point, k is a constant, and H' is the CSI matrix after the abnormal point is eliminated.
After the outliers are eliminated, median filtering is used to eliminate the noise:
Figure BDA0001470740410000081
m is a constant, and m is a constant,
Figure BDA0001470740410000082
the CSI data after noise reduction.
And 4, step 4: calculating the correlation coefficient on the frequency domain when the scene is not human and the target exists, generating Probability Density Function (PDF),and determining a threshold value according to the intersection point of the PDF images. Used in the step
Figure BDA0001470740410000083
And the subcarrier vector in the CSI matrix received for the ith subcarrier.
Step 4.1: under the condition of no person in the scene, calculating the correlation coefficient between every two subcarriers, and generating a static fingerprint C by data received by each receiving antennastaComprises the following steps:
Figure BDA0001470740410000084
Figure BDA0001470740410000085
wherein the content of the first and second substances,
Figure BDA0001470740410000086
the correlation coefficient between the ith subcarrier and the (i + 1) th subcarrier in the scene unmanned state is shown.
Step 4.2: under the state of having static object, the related coefficient is calculated, the fingerprint of the static object is generated by the data received by each receiving antenna
Figure BDA0001470740410000087
Comprises the following steps:
Figure BDA0001470740410000088
Figure BDA0001470740410000089
Figure BDA00014707404100000810
wherein the content of the first and second substances,
Figure BDA00014707404100000811
for the correlation coefficient between the ith subcarrier and the (i + 1) th subcarrier in the presence of a stationary target state, Cstan_lIs a fingerprint vector at a measurement point, and l is a measurement location point.
Step 4.3: under the condition of existence of dynamic target, calculating correlation coefficient, and generating fingerprint with dynamic target by data received by each receiving antenna
Figure BDA0001470740410000091
Comprises the following steps:
Figure BDA0001470740410000092
Figure BDA0001470740410000093
Figure BDA0001470740410000094
wherein, the first and the second end of the pipe are connected with each other,
Figure BDA0001470740410000095
for the correlation coefficient between the ith subcarrier and the (i + 1) th subcarrier in the presence of a dynamic target state, Cdyn_lIs a fingerprint vector at a measurement point, and l is a measurement location point.
Step 4.4 reaction of Csta、
Figure BDA0001470740410000096
And
Figure BDA0001470740410000097
the probability density function PDF is generated in a graph, and the distinguishing between the unmanned state and the manned state is obvious, and the distinguishing between the static object and the dynamic object is not obvious, so the numerical values of the highest points of the PDF images are compared, and the boundary between the manned state and the unmanned state of the scene is determined by the threshold value delta.
Step 5: and using a sliding window to further judge the activity state of the indoor target by drawing the probability distribution histogram of the characteristic fingerprint when a static target and a dynamic target exist in the scene and comparing the probability distribution histogram of the characteristic fingerprint processed by the data collected in the online stage. In the step, 5 seconds are selected as a sliding window to calculate m phase relation values,
Figure BDA0001470740410000098
and the subcarrier vector in the CSI matrix received for the jth subcarrier.
Step 5.1: characteristic fingerprint C generated by data received by each receiving antenna when a static target existsstan_aveComprises the following steps:
Figure BDA0001470740410000099
Figure BDA00014707404100000910
wherein the content of the first and second substances,
Figure BDA00014707404100000911
the average value of the characteristic fingerprint among the subcarriers under each sliding window is obtained.
Step 5.2: characteristic fingerprint C generated by data received by each receiving antenna when dynamic targets existdyn_aveComprises the following steps:
Figure BDA0001470740410000101
Figure BDA0001470740410000102
wherein, the first and the second end of the pipe are connected with each other,
Figure BDA0001470740410000103
for the inter-subcarrier bit under each sliding windowThe mean value of the fingerprint is characterized.
Step 6: collecting a group of online data with any time length, and calculating a correlation coefficient matrix TT of data received by each receiving antenna in an online state:
Figure BDA0001470740410000104
Figure BDA0001470740410000105
drawing a PDF image of the matrix TT, comparing the sizes of the highest point of the image and a threshold value delta, judging whether the test state is a static state, if the current state is a scene memory target obtained by comparing the test values on not less than two antennas with the threshold value delta, continuously calculating a correlation coefficient matrix TT' on each antenna in a time-frequency domain by taking 5 seconds as a sliding window:
Figure BDA0001470740410000106
Figure BDA0001470740410000107
and respectively drawing a probability distribution histogram of the relation number matrix TT' under the sliding window in the state of three antennas, comparing the probability distribution histogram with the probability distribution histogram when a static target and a dynamic target exist, wherein the probability distribution histogram is high in similarity and is a specific state of the target existing in the current scene.
Meanwhile, a large amount of experimental data is combined, a simulation experiment is carried out by utilizing Matlab, and the effectiveness of the method is verified. The specific flow of the offline phase is shown in fig. 5, and the specific flow of the online phase is shown in fig. 6.

Claims (4)

1. A Wi-Fi-based indoor personnel passive detection method is characterized by comprising the following steps:
the method comprises the following steps: in the off-line stage, reasonable indoor measuring point distribution is established, a scene is divided into a plurality of areas after obstacles are removed, and data of measuring points are collected when a static target is positioned; secondly, collecting data walking randomly in a measuring area during dynamic target; finally, data when no target to be measured exists in the scene are collected, data collection in an off-line stage is completed, and data of a part of measuring points are selected for off-line training, so that training cost is reduced;
step two: deriving measured raw data;
step three: carrying out noise reduction processing on the original data, removing abnormal points through a filter, and simultaneously eliminating noise mixed in the original data due to multipath effect and environmental influence in the CSI propagation process by using median filtering;
step four: calculating fingerprint C generated by correlation coefficient on time-frequency domain when scene is not human, when static target exists and when dynamic target existssta、
Figure FDA0003506796790000011
And
Figure FDA0003506796790000012
generating a probability density function PDF, and determining a threshold value delta according to the intersection point of PDF images;
step five: continuously analyzing the characteristic fingerprints of static targets and dynamic targets in the scene by using a sliding window, calculating sliding window correlation coefficient matrixes TT and TT 'of each receiving antenna by using the sliding window, and drawing frequency distribution histograms of TT and TT';
step six: and in the online stage, acquiring a group of online data with any duration, calculating a correlation coefficient matrix TT of data received by each receiving antenna in an online state, drawing a PDF image of the matrix TT, comparing the maximum point of the PDF image of the matrix TT with a threshold value delta, judging whether the test state is a static state, if the current state is a scene existing target according to the comparison result of the test values of not less than two antennas and the threshold value delta, continuously calculating a correlation coefficient matrix TT 'of each antenna in a sliding window in 5 seconds, respectively drawing probability distribution histograms of the correlation coefficient matrix TT' in the three antenna states, and comparing the probability distribution histograms with the probability distribution histogram with the static target and the dynamic target, wherein the high similarity is the specific state of the target existing in the current scene.
2. Wi-Fi based passive detection method for indoor people according to claim 1, wherein the filter is a hampel filter.
3. The Wi-Fi based passive detection method for indoor people according to claim 1, wherein step five comprises:
step 5.1: characteristic fingerprint C generated by data received by each receiving antenna when a static target existsstan_aveComprises the following steps:
Figure FDA0003506796790000013
Figure FDA0003506796790000014
wherein, the first and the second end of the pipe are connected with each other,
Figure FDA0003506796790000015
averaging the characteristic fingerprints between subcarriers under each sliding window;
and step 5.2: characteristic fingerprint C generated by data received by each receiving antenna when dynamic targets existdyn_aveComprises the following steps:
Figure FDA0003506796790000021
Figure FDA0003506796790000022
wherein，
Figure FDA0003506796790000023
Is the average value of the characteristic fingerprints among the subcarriers under each sliding window.
4. Wi-Fi based passive detection method for indoor people according to claim 1, wherein the sliding window in step five is 5 seconds.
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