CN110022530B - Wireless positioning method and system for underground space - Google Patents

Wireless positioning method and system for underground space Download PDF

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CN110022530B
CN110022530B CN201910203175.5A CN201910203175A CN110022530B CN 110022530 B CN110022530 B CN 110022530B CN 201910203175 A CN201910203175 A CN 201910203175A CN 110022530 B CN110022530 B CN 110022530B
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wireless access
positioning
target object
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rssi
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CN110022530A (en
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王巍
黄森
张宁
王盼
江涛
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Huazhong University of Science and Technology
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/10—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration
    • G01C21/12—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning
    • G01C21/16—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation
    • G01C21/165—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation combined with non-inertial navigation instruments
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S5/00—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
    • G01S5/02—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using radio waves
    • G01S5/0294—Trajectory determination or predictive filtering, e.g. target tracking or Kalman filtering
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/02—Services making use of location information
    • H04W4/021—Services related to particular areas, e.g. point of interest [POI] services, venue services or geofences
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/30—Services specially adapted for particular environments, situations or purposes
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W64/00—Locating users or terminals or network equipment for network management purposes, e.g. mobility management
    • H04W64/006—Locating users or terminals or network equipment for network management purposes, e.g. mobility management with additional information processing, e.g. for direction or speed determination

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  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
  • Computer Networks & Wireless Communication (AREA)
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  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Automation & Control Theory (AREA)
  • Position Fixing By Use Of Radio Waves (AREA)
  • Mobile Radio Communication Systems (AREA)

Abstract

The invention discloses a wireless positioning method and a wireless positioning system for an underground space, wherein the wireless positioning method comprises the following steps: arranging wireless access points in an underground space and constructing a fingerprint database; acquiring RSSI information of the position of a target object and matching the RSSI information with a fingerprint database to obtain a predicted value of the position of the target object, acquiring inertial data and carrying out dead reckoning to obtain a measured value of the position of the target object; fusing the predicted value and the measured value by adopting Kalman filtering to obtain the position of a target person; and when no target object exists in the underground space, processing the CSI data sent by the wireless access point, and detecting whether illegal intrusion exists in real time. According to the invention, the RSSI fingerprint positioning and the PDR positioning are fused through Kalman filtering, so that respective defects of the RSSI fingerprint positioning and the PDR positioning are overcome, and accurate positioning in a narrow closed underground space with a long distance can be realized; the method can be realized by only deploying a small number of wireless access points and combining with a smart phone with wide application, and is low in cost and simple to operate.

Description

Wireless positioning method and system for underground space
Technical Field
The invention belongs to the technical field of wireless communication, and particularly relates to a wireless positioning method and system for an underground space.
Background
As is well known, the rapid development of the economy of China, the construction and maintenance of urban underground space become key elements for guaranteeing the harmony and stability of cities. Such as subway construction, comprehensive pipe gallery construction, submarine tunnel construction and the like, regular maintenance of personnel is needed, the areas belong to sensitive dangerous areas, ubiquitous communication infrastructure on the ground is not covered, the position and state information of target personnel in the areas are determined, the protection of maintenance personnel is facilitated, comprehensive real-time detection and scheduling are realized, and the safety of the areas is guaranteed.
The current Wireless positioning technology mainly includes technologies based on bluetooth, ZigBee, UWB (Ultra Wide Band), Radio Frequency Identification (RFID), Wireless Local Area Network (WLAN), and the like. In recent years, wireless local area networks have been greatly developed by virtue of advantages such as long distance, high frequency spectrum utilization rate and interference resistance, and the number of related intelligent devices is rapidly increased, so that the positioning and personnel detection technology based on the wireless local area networks has also obtained wide application space.
Generally, an RSSI fingerprint positioning algorithm used in positioning based on a wireless local area network has the disadvantage that the positioning accuracy is lower in a narrow closed underground space with a longer distance because the RSSI of an area farther away from a wireless access point does not change obviously with the increase of the distance due to the limitation of equipment. The existing underground space positioning system mostly ensures the positioning accuracy by deploying intensive signal nodes or adopting customized equipment, however, the excessive number of nodes or the requirement on the customized equipment bring higher cost investment.
In the aspect of personnel detection, the conventional common methods mainly comprise microwave blocking detection, ultrasonic detection, infrared detection, acceleration motion detection, real-time camera analysis and the like, and the methods are realized by additionally introducing special equipment such as a microwave detection module, an ultrasonic detection module, an infrared heat release module or an acceleration sensor module and the like, so that the cost is high.
Therefore, the problems of high cost and low positioning precision exist in the conventional underground space wireless positioning method.
Disclosure of Invention
Aiming at the defects of the prior art, the invention aims to provide a wireless positioning method and a wireless positioning system for an underground space, and aims to solve the problem of low positioning accuracy of the existing wireless positioning method for the underground space.
In order to achieve the above object, the present invention provides a wireless positioning method for an underground space, comprising the steps of:
(1) arranging wireless access points in an underground space and constructing a fingerprint database;
(2) acquiring RSSI information of the position of a target object and matching the RSSI information with a fingerprint database to obtain a predicted value of the position of the target object, acquiring inertial data and carrying out dead reckoning to obtain a measured value of the position of the target object;
(3) fusing the predicted value and the measured value by adopting Kalman filtering to obtain the position of a target person;
(4) and when no target object exists in the underground space, processing the CSI data sent by the wireless access point, and detecting whether illegal intrusion exists in real time.
Further, the distance between the wireless access points in the step (1) is set to be within 100 meters.
Further, the constructing a fingerprint database in the step (1) specifically includes:
performing equal-interval area division among wireless access points, and selecting the central points of the divided areas as fingerprint points;
and acquiring RSSI information of the wireless access point at the fingerprint point, uploading the information to a server, and constructing a fingerprint database.
Further, the fusion method in the step (3) specifically includes:
and substituting the predicted value and the measured value into a state equation and an updating equation to carry out Kalman filtering:
Xn＝Xn-1+K(Zn-Xn-1)
K＝P/(P+R)
P＝P-KQ+Q
wherein, XnIs a state vector representing the current state of the system, i.e. the target position, X, obtained after fusionn-1For the position of a time instant on the system, K is the filter gain, ZnFor RSSI fingerprint positioning results, i.e. the predicted values, P is the systemThe variance of the error is 1 in the initial value, R is the variance of the pedestrian dead reckoning, and Q is the variance of the system noise.
Before receiving RSSI fingerprint positioning result, XnThe initial value of the system is a pedestrian track calculation result, namely a measured value, and the system fuses the position at the last moment and the current RSSI fingerprint positioning result to obtain a new target position.
Further, the system noise Q is automatically adjusted according to the position of the target object to make up the respective defects of RSSI fingerprint positioning and pedestrian track calculation and obtain an accurate fusion positioning result; the method for automatically adjusting the size of the system noise Q according to the position of the target object specifically comprises the following steps:
when the target object moves to the wireless access point, the Q value is reduced, and the fusion result is close to the RSSI fingerprint positioning result; and when the target object is far away from the wireless access point, the Q value is increased, and the fusion result is close to the pedestrian dead reckoning and positioning result.
Further, by the above method, the processing CSI data sent by the wireless access point in the step (4) specifically includes:
(41) obtaining and storing amplitudes of CSI information on n data streams between wireless access points within a time period t; wherein n is the product of the number of wireless access point antennas;
(42) processing the respective amplitudes by using a Pearson correlation coefficient, and summing the obtained n Pearson correlation coefficients;
(43) the sum of all the obtained Pearson correlation coefficients is saved as a vector { P } within a set reaction timetPt+1…Pt+kK is the number of the sum of Pearson coefficients obtained in the reaction time;
(44) calculating a median absolute difference MAD;
(45) comparing the median absolute difference with a threshold, and when the median absolute difference MAD is greater than or equal to the threshold, invading by people; when the median absolute difference is less than a threshold, then no human intrusion.
Further, the method for calculating the median absolute difference MAD in the step (44) is:
calculating vector { PtPt+1…Pt+kMedian of the sum of all pearson coefficients in the};
calculating a deviation between the sum of each pearson coefficient and the median;
and (5) solving the median MAD of all deviation absolute values.
Preferably, the threshold value in the step (45) is in the range of 0.08-0.25.
Furthermore, the target object is a legal person carrying intelligent equipment, and the intelligent equipment can communicate with the wireless access point and can acquire and send RSSI information and inertia measurement data.
Preferably, the smart device is a smart phone.
In another aspect, the present invention provides a wireless location system for an underground space, comprising:
the data acquisition module is used for acquiring RSSI information and inertia measurement data of the position of the target object;
the data processing module is used for matching the RSSI information with a fingerprint database to obtain a predicted value of the position of the target object, and carrying out dead reckoning by using the inertia measurement data to obtain a measured value of the position of the target object;
the fusion positioning module is used for fusing the predicted value and the measured value by using Kalman filtering to obtain the position of the target person;
and the intrusion detection module is used for solving a Pearson correlation coefficient from the CSI data acquired by the wireless access point, and setting a proper median absolute difference threshold value so as to detect whether illegal intrusion exists in real time.
Through the technical scheme, compared with the prior art, the invention has the following beneficial effects:
(1) according to the invention, the position detection of legal personnel can be realized by deploying a small number of wireless access points and combining with a smart phone with wide application, so that the cost is low and the operation is simple.
(2) According to the invention, the RSSI fingerprint positioning and the PDR positioning are fused through Kalman filtering, so that respective defects of the RSSI fingerprint positioning and the PDR positioning are overcome, and accurate positioning in a narrow closed underground space with a long distance can be realized.
(3) The invention utilizes the Pearson correlation coefficient to process the CSI information amplitude value sent by the wireless access, and can simply, conveniently and accurately detect whether a person invades the underground space or not by setting threshold value comparison, thereby having better security protection effect.
Drawings
Fig. 1 is a schematic flow chart of a method for wireless positioning in an underground space according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a method for deploying wireless access points in a subterranean space according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a fusion positioning using Kalman filtering according to an embodiment of the present invention;
fig. 4 is a schematic diagram of Median Absolute Difference (MAD) of pearson correlation coefficients in an unmanned state and a human-invasive state according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
In one aspect, the embodiment of the present invention provides a wireless positioning method for an underground space, and referring to fig. 1, the method flow includes the following steps:
step 1: arranging wireless access points in an underground space and constructing a fingerprint database;
as shown in fig. 2, a wireless access point 1 and a wireless access point 2 which are 80 meters apart are arranged in an underground space, an area division is performed between the two wireless access points at equal intervals, a block area central point is selected as a fingerprint acquisition point label, an intelligent device such as a smart phone is adopted to acquire RSSI information of the two wireless access points at the fingerprint point and upload the RSSI information to a server side, and a fingerprint library is constructed. In actual operation, the obtainedThe RSSI information is stored as a fingerprint library matrix L, and the content of the ith row of the fingerprint library matrix is
Figure BDA0001998144870000061
Wherein
Figure BDA0001998144870000062
Respectively, the RSSI information of the wireless access point 1 and the wireless access point 2 at the fingerprint point.
Step 2: acquiring RSSI information of the position of a target object and matching the RSSI information with a fingerprint database to obtain a predicted value of the position of the target object, acquiring inertial data and carrying out pedestrian track calculation to obtain a measured value of the position of the target object;
specifically, the RSSI information can be acquired and transmitted by the smart phone, the inertial measurement data is measured by an inertial measurement unit comprising a gyroscope and an accelerator, and the inertial measurement unit can also be arranged on the smart phone and acquired and transmitted by the smart phone for convenient carrying and operation; fingerprint matching is carried out on the RSSI information by utilizing a K neighbor algorithm to obtain the predicted position of the target person; and (4) finishing pedestrian track calculation by using the inertial measurement data to obtain a measured value of the position of the target object.
And step 3: fusing the predicted value and the measured value by adopting Kalman filtering to obtain the position of a target person;
specifically, on one hand, the RSSI information of areas farther away from the wireless access point does not change significantly with increasing distance, so that the positioning cannot be performed in these areas; on one hand, the result obtained by the pedestrian track estimation is a relative value, which cannot be directly converted into a distance and has accumulated errors. In order to solve the above problems, as shown in fig. 3, the present invention adopts kalman filtering to fuse the results obtained in step 2, and the kalman filtering has the main advantage that the optimal estimation value can be obtained by combining the predicted value (RSSI information) and the observed value (pedestrian track calculation), and is suitable for being used in a continuously changing system, and has small occupied memory and high operation speed.
The one-dimensional Kalman filtering state equation and the updating equation adopted by the invention are as follows:
Xn＝Xn-1+K(Zn-Xn-1)
K＝P/(P+R)
P＝P-KQ+Q
wherein, XnIs a state vector representing the current state of the system, i.e. the target position, X, obtained after fusionn-1For the position of a time instant on the system, K is the filter gain, ZnAnd (3) obtaining an RSSI fingerprint positioning result, namely a Kalman filtering predicted value, wherein P is the variance of a system error, the initial value is 1, R is the variance of the pedestrian dead reckoning, and Q is the variance of system noise.
Before receiving RSSI fingerprint positioning result, XnThe initial value of the system is a pedestrian track calculation result, namely a Kalman filtering measurement value, and the system fuses the position at the last moment and the current fingerprint positioning result to obtain a new target position.
In the traditional Kalman filtering theory, the Q value is constant, but the fusion result can be obviously influenced by the variation of the Q value, the larger the Q value is, the more trusted the pedestrian track dead reckoning result is, the closer the Q value is to 0, the more trusted the RSSI positioning result is, the constant Q value cannot achieve the purpose of eliminating the fixed error, and the Kalman filtering fusion effect is not ideal. According to the method, the system noise Q is automatically adjusted according to the position of the target object, so that the respective defects of RSSI fingerprint positioning and Pedestrian Dead Reckoning (PDR) are overcome, and an accurate fusion positioning result is obtained. The method for automatically adjusting the size of the system noise Q specifically comprises the following steps:
by analyzing the result of Kalman filtering fusion, if the person moves to an area close to the wireless access point, namely an RSSI (received signal strength indicator) positioning accurate area, the Q value is automatically reduced; the Q value will automatically increase when a person moves to an area that is relatively far from the wireless access point.
And 4, step 4: and when no person exists in the space to be detected, processing the CSI data sent by the wireless access point, and detecting whether illegal intrusion exists in real time.
Specifically, research shows that when no person is in the space to be measured and the space enters a closed State, if an intruder exists, Channel State Information (CSI) data from wireless access points fluctuates greatly compared with the unattended State, and the method processes the CSI Information based on the above steps:
obtaining and storing amplitudes of CSI information on n data streams between wireless access points within a time period t; wherein n is the product of the number of two wireless access point antennas;
processing the respective amplitudes by using a Pearson correlation coefficient, and summing the obtained n Pearson correlation coefficients;
the overall process is as follows:
Figure BDA0001998144870000081
wherein, PtIs the sum of Pearson's correlation coefficients, Xi、XjThe CSI information amplitude on the ith and jth data streams respectively,
Figure BDA0001998144870000082
standard deviation of the amplitude of the CSI information on the ith and jth data streams, cov (X), respectivelyi,Xj) Is Xi、XjThe covariance of (a).
The sum of all the obtained Pearson correlation coefficients is saved as a vector { P } within a set reaction timetPt+1…Pt+kAnd k is the number of the sum of the Pearson coefficients obtained in the reaction time.
And comparing the absolute difference of the median with a threshold value to judge whether the space is invaded by people.
The median absolute difference calculation process comprises the following steps: calculating the median of the sum of all Pearson coefficients in the vector; calculating the deviation between the sum of each pearson coefficient and the median; and (5) calculating the median of all the deviation absolute values. As shown in fig. 4, when no one is in the enclosed space, the MAD is small; when someone invades, the MAD is large. In order to accurately judge whether a person invades or not within the set reaction time, comparing the MAD with a proper threshold value threshold, and when the MAD is greater than or equal to the threshold value threshold, the person invades; when MAD is less than threshold, then no human intrusion. The threshold value range is as follows: 0.08-0.25.
In another aspect, an embodiment of the present invention provides a wireless positioning system for an underground space, including:
the data acquisition module is used for acquiring RSSI information and inertia measurement data of the position of the target object;
the data processing module is used for matching the RSSI information with a fingerprint database to obtain a predicted value of the position of the target object, and carrying out dead reckoning by using the inertia measurement data to obtain a measured value of the position of the target object;
the fusion positioning module is used for fusing the predicted value and the measured value by using Kalman filtering to obtain the position of the target person;
and the intrusion detection module is used for solving a Pearson correlation coefficient from the CSI data acquired by the wireless access point, and setting a proper median absolute difference threshold value so as to detect whether illegal intrusion exists in real time.
In the embodiment of the present invention, the specific implementation manner of each module may refer to the description in the corresponding method embodiment, and the embodiment of the present invention will not be repeated.
It will be understood by those skilled in the art that the foregoing is only a preferred embodiment of the present invention, and is not intended to limit the invention, and that any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the scope of the present invention.

Claims (9)

1. A wireless location method for an underground space, comprising the steps of:
(1) arranging wireless access points in an underground space and constructing a fingerprint database;
(2) acquiring RSSI information of the position of a target object and matching the RSSI information with a fingerprint database to obtain a predicted value of the position of the target object, acquiring inertial data and carrying out dead reckoning to obtain a measured value of the position of the target object;
(3) fusing the predicted value and the measured value by adopting Kalman filtering to obtain the position of a target person;
(4) when no target object exists in the underground space, the CSI data sent by the wireless access point is processed, and whether illegal invasion exists or not is detected in real time; the processing of the CSI data sent by the wireless access point in the step (4) specifically includes:
(41) obtaining and storing amplitudes of CSI information on n data streams between wireless access points within a time period t; wherein n is the product of the number of wireless access point antennas;
(42) respectively processing the amplitudes by adopting Pearson correlation coefficients, and summing the obtained n Pearson correlation coefficients;
(43) the sum of all the obtained Pearson correlation coefficients is saved as a vector { P } within a set reaction timetPt+1…Pt+kK is the number of the sum of Pearson coefficients obtained in the reaction time;
(44) calculating a median absolute difference MAD;
(45) comparing the median absolute difference with a threshold, and when the median absolute difference MAD is greater than or equal to the threshold, invading by people; when the median absolute difference is less than a threshold, then no human intrusion.
2. A wireless positioning method for underground space according to claim 1, wherein the distance between the wireless access points in step (1) is set to be within 100 meters.
3. The wireless positioning method for underground space according to claim 1 or 2, wherein the constructing a fingerprint database in step (1) specifically comprises:
performing equal-interval area division among wireless access points, and selecting the central points of the divided areas as fingerprint points;
and acquiring RSSI information of the wireless access point at the fingerprint point, uploading the information to a server, and constructing a fingerprint database.
4. The wireless positioning method for underground space according to claim 1, wherein the fusion method in the step (3) is specifically:
and substituting the predicted value and the measured value into the following equation to carry out Kalman filtering:
Xn＝Xn-1+K(Zn-Xn-1)
K＝P/(P+R)
P＝P-KQ+Q
wherein, XnIs a state vector representing the current state of the system, i.e. the target position, X, obtained after fusionn-1For the position of a time instant on the system, K is the filter gain, ZnFor the RSSI fingerprint positioning result, namely the predicted value, P is the variance of the system error, the initial value is 1, R is the variance of the pedestrian dead reckoning, and Q is the variance of the system noise;
before receiving RSSI fingerprint positioning result, XnThe initial value of the RSSI fingerprint positioning system is a pedestrian track calculation result, and the position of the last moment is fused with the current RSSI fingerprint positioning result to obtain a new target position.
5. A wireless location method for a subterranean space according to claim 4, wherein the system noise Q is automatically resized according to:
when the target object moves to the wireless access point, the Q value is reduced, and the fusion result is closer to the RSSI fingerprint positioning result; when the target object is far away from the wireless access point, the Q value is increased, and the fusion result is closer to the pedestrian dead reckoning and positioning result.
6. A method as claimed in claim 1, wherein the median absolute difference MAD in step (44) is calculated by:
find the vector { PtPt+1…Pt+kMedian of the sum of all pearson coefficients in the};
calculating the deviation between the sum of each pearson coefficient and the median;
and (5) solving the median MAD of all deviation absolute values.
7. A method as claimed in claim 1, wherein said threshold value in step (45) is in the range of 0.08-0.25.
8. The wireless positioning method for underground space of claim 1, wherein the target object is a legal person carrying a smart device, and the smart device can communicate with the wireless access point and can acquire and transmit RSSI information and inertial measurement data.
9. A wireless location system for a subterranean space, comprising:
the data acquisition module is used for acquiring RSSI information and inertia measurement data of the position of the target object;
the data processing module is used for matching the RSSI information with a fingerprint database to obtain a predicted value of the position of the target object, and carrying out dead reckoning by using the inertia measurement data to obtain a measured value of the position of the target object;
the fusion positioning module is used for fusing the predicted value and the measured value by using Kalman filtering to obtain the position of the target person;
the intrusion detection module is used for solving a Pearson correlation coefficient from CSI data acquired by the wireless access point and setting a proper median absolute difference threshold so as to detect whether illegal intrusion exists in real time; the method comprises the following steps of solving a Pearson correlation coefficient of CSI data acquired by a wireless access point, setting a proper median absolute difference threshold value, and further detecting whether illegal intrusion exists in real time, wherein the method specifically comprises the following steps:
obtaining and storing amplitudes of CSI information on n data streams between wireless access points within a time period t; wherein n is the product of the number of wireless access point antennas;
respectively processing the amplitudes by adopting Pearson correlation coefficients, and summing the obtained n Pearson correlation coefficients;
the sum of all the obtained Pearson correlation coefficients is saved as a vector { P } within a set reaction timetPt+1…Pt+kK is the number of the sum of Pearson coefficients obtained in the reaction time;
calculating a median absolute difference MAD;
comparing the median absolute difference with a threshold, and when the median absolute difference MAD is greater than or equal to the threshold, invading by people; when the median absolute difference is less than a threshold, then no human intrusion.
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Families Citing this family (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110597108B (en) * 2019-08-23 2021-12-21 广州电力设计院有限公司 Cable tunnel area control system, control method and device and computer equipment
CN111726757A (en) * 2020-05-25 2020-09-29 南京理工大学 Indoor parking lot positioning and navigation method based on Bluetooth
CN113207082B (en) * 2021-03-30 2021-11-26 重庆越致科技有限公司 Mobile network data positioning system and method based on traffic route position fingerprint database
CN115407211B (en) * 2022-11-01 2023-01-31 北京航空航天大学 Online prediction method and system for health state of lithium battery of electric vehicle

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104655137A (en) * 2015-03-05 2015-05-27 中国人民解放军国防科学技术大学 Wi-Fi signal fingerprint positioning algorithm for assisting in speculating flight tracks of pedestrians
CN105931124A (en) * 2016-05-19 2016-09-07 中国地质大学（武汉） Indoor social network service system
CN106597363A (en) * 2016-10-27 2017-04-26 中国传媒大学 Pedestrian location method in indoor WLAN environment
CN109298389A (en) * 2018-08-29 2019-02-01 东南大学 Indoor pedestrian based on multiparticle group optimization combines position and orientation estimation method

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104655137A (en) * 2015-03-05 2015-05-27 中国人民解放军国防科学技术大学 Wi-Fi signal fingerprint positioning algorithm for assisting in speculating flight tracks of pedestrians
CN105931124A (en) * 2016-05-19 2016-09-07 中国地质大学（武汉） Indoor social network service system
CN106597363A (en) * 2016-10-27 2017-04-26 中国传媒大学 Pedestrian location method in indoor WLAN environment
CN109298389A (en) * 2018-08-29 2019-02-01 东南大学 Indoor pedestrian based on multiparticle group optimization combines position and orientation estimation method

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
低成本高精度地下空间无线定位与人员检测系统;黄森等;《2018中国信息通信大会论文摘要集》;20191214;摘要 *

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