CN105873212A - Indoor-environment-person detection method based on channel state information - Google Patents

Indoor-environment-person detection method based on channel state information Download PDF

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Publication number
CN105873212A
CN105873212A CN201610323973.8A CN201610323973A CN105873212A CN 105873212 A CN105873212 A CN 105873212A CN 201610323973 A CN201610323973 A CN 201610323973A CN 105873212 A CN105873212 A CN 105873212A
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signal
csi
information
indoor environment
indoor
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Inventor
肖甫
谢晓辉
朱海
陈静
孙力娟
王汝传
郭剑
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Nanjing Post and Telecommunication University
Nanjing University of Posts and Telecommunications
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Nanjing Post and Telecommunication University
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    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04WWIRELESS COMMUNICATION NETWORKS
    • H04W64/00Locating users or terminals or network equipment for network management purposes, e.g. mobility management
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04LTRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L5/00Arrangements affording multiple use of the transmission path
    • H04L5/003Arrangements for allocating sub-channels of the transmission path
    • H04L5/0048Allocation of pilot signals, i.e. of signals known to the receiver
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04LTRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L5/00Arrangements affording multiple use of the transmission path
    • H04L5/0091Signaling for the administration of the divided path
    • H04L5/0094Indication of how sub-channels of the path are allocated
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04LTRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00Network arrangements or protocols for supporting network services or applications
    • H04L67/01Protocols
    • H04L67/12Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks

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  • Engineering & Computer Science (AREA)
  • Signal Processing (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Health & Medical Sciences (AREA)
  • Computing Systems (AREA)
  • General Health & Medical Sciences (AREA)
  • Medical Informatics (AREA)
  • Mobile Radio Communication Systems (AREA)

Abstract

The invention discloses omni-directional-person detection method suitable for the indoor environment based on GSI. Received signal strength indicator (RSSI) information is mainly adopted in the traditional indoor person detection scheme based on WiFi; the RSSI information can be conveniently measured through common commercial AP and a WiFi terminal, but the coarse granularity defect, the poor time stability defect and the like exist in the RSSI information. According to the problem, RSSI is replaced with channel state information (GSI), The GSI has the advantages of being good in time stability, high in dynamic-environment interference resistance and sensibility to link periphery persons and the like, an industrial control computer based on an Intel 5300-series network card is carried in the indoor environment, the fingerprint matching method is adopted, and indoor omni-directional passive person perception is achieved. By means of the method, the accurate rate of person detection in the indoor environment can be increased, and meanwhile omni-directional-person detection in the indoor environment can be achieved.

Description

Indoor environment personnel's detection method based on channel condition information
Technical field
The present invention is a kind of personnel's detection method being applicable under indoor environment, belongs to technology of Internet of things field.
Background technology
Along with developing rapidly of computer and communication technology, calculate resource and will spread all over the environment around people, context aware Technology is arisen at the historic moment.After context aware obtains the environmental information of sensor acquisition, information is carried out Intelligent treatment neatly Provide the user service.WiFi signal becomes the research in situation of presence cognition technology as the infrastructure of a kind of widespread deployment Focus.But WiFi signal narrower bandwidth, temporal resolution is low, there is bigger gap, therefore need badly on signal handling equipment Novel cognition technology under design WiFi environment and method, it is achieved the accurate ring of indoor environment based on general commercial WiFi equipment Border perception.
WiFi indoor positioning is mainly adopted as the typical case's application in context aware, tradition indoor orientation method based on WiFi Use RSSI information, i.e. receive signal designation intensity.Common commercial AP and WiFi terminal can measure RSSI information easily, But rssi measurement is the signal after multipath superposition, it is impossible to fine granularity ground is distinguished each paths signal and propagated information, Under complicated indoor environment, the little yardstick shadow fading caused by the multipath transmisstion of signal will greatly limit the range finding essence of RSSI Degree and stability.Therefore, it is necessary to further investigation is launched in the location under indoor environment and cognition technology, find more fine granularity Signal portray the factor, to obtain indoor positioning and perceptual performance the most accurately.Meaning of the present invention is: devise one Indoor omnidirectional based on CSI passive type person sensitive's method, for realizing the essence under indoor environment based on general commercial WiFi equipment Really context aware provides scheme.
Summary of the invention
Technical problem: it is an object of the invention to provide high-precision personnel's detection method under a kind of indoor environment, to realize High-precision environment sensing in general commercial WiFi equipment.This programme uses physical layer channel conditions information (CSI) to replace passing Reception signal designation strength information (RSSI) of system, by carrying merit based on Intel 5300 series network interface card under indoor environment Control machine, the method using fingerprint matching, utilize grey Relational Analysis Method to carry out Signal Matching, it is achieved that indoor omnidirectional is passive Formula person sensitive.The method that the application of the invention proposes can solve RSSI coarse size, time stability difference and cannot distinguish between The defects such as multipath signal propagations information, it is achieved more fine granularity and accurately indoor occupant detection scheme.
Technical scheme: the present invention utilizes physical layer channel conditions information (CSI) to replace conventional receiver signal designation intensity letter Breath (RSSI), utilize CSI information time good stability, strong to dynamic environment interference resistance, link surrounding people is existed quick The advantages such as perception is strong, by carrying power control machine based on Intel 5300 series network interface card under indoor environment, use fingerprint matching Method, utilize grey Relational Analysis Method to carry out Signal Matching, it is achieved that the perception of omnidirectional's passive type personnel under indoor environment Detection.
The present invention is a kind of personnel's detection scheme being applicable under indoor environment, for tradition indoor occupant based on RSSI There is coarse size, time stability difference and cannot distinguish between the defects such as mulitpath information in detection scheme, the program utilizes physics Layer channel condition information (CSI) replaces RSSI, utilizes CSI information time good stability, dynamic environment is disturbed resistance strong, right There is the advantages such as sensitivity is strong in link surrounding people, by carrying merit based on Intel 5300 series network interface card under indoor environment Control machine, the method using fingerprint matching, utilize grey Relational Analysis Method to carry out Signal Matching, it is achieved that indoor omnidirectional is passive Formula person sensitive.
Should omnidirectional's personnel's detection scheme being applicable under indoor environment based on CSI, be included in detail below in step:
CSI signal characteristic abstraction and pretreatment:
Step 1) carry out CSI signal data acquisition in indoor environment: verification platform includes installing Ubuntu system, Intel One, the mini power control machine of 5300 wireless network cards, virtual CSI and CSI Tool instrument, TP-Link router, external sky Line is some, LCDs, notebook computer are some.In scheme, TP-Link leads to as signal emitting-source AP, mini power control machine Crossing 5300 network interface cards and receive wireless signal, external antenna forms a link as signal receiver MP, every a pair AP, MP.Utilize Virtual CSI and CSI Tool instrument gather transmitting channel state information and are saved in notebook;
Step 2) utilize Fourier inversion that the frequency-region signal CFR collected is converted into time-domain signal CIR;
Step 3) CIR data message is processed, reduce multipath transmisstion and the impact of shadow fading;
Step 4) CIR data signal after processing is carried out Fourier transformation, it is reduced into frequency-region signal CFR;
Step 5) utilize the CFR signal data obtained to obtain amplitude and the phase information of channel condition information;
Received signals fingerprint Database:
Step 6) for different scenes, gather CSI signal and be trained, choose four direction respectively and (respectively go up bottom left Right four direction, as the simple abstract of omnidirectional) reference point carry out the measurement of signal characteristic in the case of artificial disturbance and add In fingerprint matching data base, add nobody exist in the case of signal characteristic, complete the foundation of fingerprint matching data base;
Omnidirectional personnel detect:
Step 7) utilize the grey relational grade analysis method in statistics to carry out the coupling of signal characteristic, select the degree of association High data field signal feature is as when the signal characteristic of pre-test scene.
So far, it is achieved the detection of omnidirectional personnel and perception under indoor environment.
Some key operations involved in above step are defined as follows:
Signal Pretreatment thought:
Owing under indoor environment, the propagation of signal exists multipath effect and the phenomenon of shadow fading and protected from environmental Ratio is more serious, so needing CSI signal to be carried out pretreatment, to alleviate before utilizing the CSI signal gathered to carry out personnel's detection Multipath effect and the impact of shadow fading.
The Signal Pretreatment that the present invention uses specifically comprises the following steps that
Step 1) utilize IFFT Fourier transformation that the frequency-region signal CFR collected is converted into time-domain signal CIR;
Step 2) CIR signal is divided into limited wavestrip, the available bandwidth of a length of experimental facilities of each wavestrip, this Time the existence that limits due to bandwidth, be difficult to the complete signal obtaining all collections in the case of time domain;
Step 3) only retain the wavestrip at amplitude maximum arriving signal place and filter other band signals, i.e. think and carry The signal of big energy and signal packet about are containing most LOS path information;
Step 4) the CIR data signal after processing is carried out FFT Fourier transformation, it is reduced into frequency-region signal CFR;
Fingerprint matching algorithm:
We introduce the fingerprint matching algorithm during grey Relational Analysis Method detects as actual person.Grey correlation Degree analytic process is the one in Grey System Analysis method, is a kind of factor method of comparative analysis.Its cardinal principle is by ash In colour system system, the analysis of finite data series is found with the factor of desired value degree of association maximum, and then analyzes the pass between each factor Connection degree.Its main distinguishing rule is the similarity degree of DS curve, the general trend of curve closer to, then illustrate two The degree of association between person is the highest.Therefore grey relational grade analysis is the tolerance mark that the changing trend of a system provides quantization Standard, is especially suitable for dynamically analyzing.
Grey correlation analysis basic step is as follows:
If target sequence is X0=(x0(1),x0(2),…,x0(n)), comparative sequences is Xi=(xi(1),xi(2),…,xi (n)), i={1,2,3 ..., m}, wherein n is sequence length, and m is comparative sequences number.
For ξ ∈ (0,1), orderThen X0With XiGrey relational grade be represented by:Concrete operation step is as follows:
(1) data prediction.Need data are carried out pretreatment when data dimension is inconsistent when so that data are immeasurable Guiding principle.We use initial value facture:
X i ′ = X i x i ( 1 ) , i = 0 , 1 , 2 , ... , m - - - ( 1 )
(2) difference sequence is sought.
Δi(k)=| x'0(k)-x′i(k)| (2)
(3) two-stage maximum difference and lowest difference are asked.Note maximum difference is M, and lowest difference is m, then:
M = m a x i m a x k Δ i ( k ) , m = min i min k Δ i ( k ) - - - ( 3 )
(4) coefficient of association is sought.
γ 0 i ( k ) = m + ξ M Δ i ( k ) + ξ M , ξ ∈ ( 0 , 1 ) , k = 1 , 2 , ... , n , i = 1 , 2 , ... , m - - - ( 4 )
Wherein, ξ is resolution ratio, that reflects the significance between grey incidence coefficient, in order to simplify calculating, typically takes 0.5。
(5) degree of association is calculated.
Compare for convenience, coefficient of association is carried out average value processing so that the information concentrated expression of coefficient of association reflection goes out Come.Then the degree of association is represented by:
γ ( X 0 , X i ) = 1 n Σ k = 1 n γ 0 i ( k ) - - - ( 5 )
(6) relational degree taxis.
Closer to 1, the value of degree of association γ represents that the degree of association of two data sequence is the highest.Grey relational grade is arranged by size Row show that each comparative sequences is with the degree of association of target sequence.
Beneficial effect: the present invention devises the omnidirectional's personnel's detection scheme under a kind of indoor environment based on CSI, counterparty Case has the following advantages:
1. simplicity
Scheme uses physical layer information CSI to detect evaluation points as personnel under indoor environment, and CSI can be from general commercial Extracting in WiFi equipment, add the widespread deployment of WiFi infrastructure, this makes to obtain CSI information and becomes simple possible.
2. adaptability
Scheme uses CSI to replace traditional RSSI, overcomes RSSI coarse size and the defect of time stability difference, CSI energy Enough obtain the most fine-grained information and mulitpath can be distinguished, can be suitably used for more indoor application scene.
The most functional
Scheme considers the directivity of detection range further, utilizes finger print matching method to realize omnidirectional's personnel's existence inspection Surveying, be compared to traditional unidirectional personnel that can only realize and detect, function is the most powerful.
4. optimization property
This programme only only accounts for the amplitude information of CSI signal, by considering the phase information of CSI signal, angle information Etc. being obtained in that the most superior personnel detect performance, may be used for realizing the degree of accuracy such as personnel positioning, gesture identification more simultaneously High application.
Accompanying drawing explanation
Fig. 1 is wireless signal multipath transmisstion schematic diagram under indoor environment;
Fig. 2 is indoor omnidirectional personnel detection scheme flow chart based on CSI.
Detailed description of the invention
The present invention is a kind of personnel's detection scheme being applicable under indoor environment, for tradition indoor occupant based on RSSI There is coarse size, time stability difference and cannot distinguish between the defects such as mulitpath information in detection scheme, the program utilizes physics Layer channel condition information (CSI) replaces RSSI, utilizes CSI information time good stability, dynamic environment is disturbed resistance strong, right There is the advantages such as sensitivity is strong in link surrounding people, by carrying merit based on Intel 5300 series network interface card under indoor environment Control machine, the method using fingerprint matching, utilize grey Relational Analysis Method to carry out Signal Matching, it is achieved that indoor omnidirectional is passive Formula person sensitive.
Should omnidirectional's personnel's detection scheme being applicable under indoor environment based on CSI, be included in detail below in step:
CSI signal characteristic abstraction and pretreatment:
Step 1) under indoor environment, carry out CSI signal data acquisition: platform includes being provided with Ubuntu system, Intel One, the mini power control machine of 5300 wireless network cards, virtual CSI and CSI Tool instrument, TP-Link router, external sky Line is some, LCDs, notebook computer are some.In an experiment, TP-Link is as signal emitting-source AP, mini power control machine Receiving wireless signal by 5300 network interface cards, external antenna forms a link as signal receiver MP, every a pair AP, MP.Profit Gather transmitting channel state information with virtual CSI and CSI Tool instrument and be saved in notebook;
Step 2) utilize IFFT Fourier transformation that the frequency-region signal CFR collected is converted into time-domain signal CIR;
Step 3) CIR data message is processed, reduce multipath transmisstion and the impact of shadow fading;
Step 4) the CIR data signal after processing is carried out FFT Fourier transformation, it is reduced into frequency-region signal CFR;
Step 5) utilize the CFR signal data obtained to obtain amplitude and the phase information of channel condition information;
Received signals fingerprint Database:
Step 6) for different scenes, gather CSI signal and be trained, choose four direction respectively and (respectively go up bottom left Right four direction, as the simple abstract of omnidirectional) reference point carry out the measurement of signal characteristic in the case of artificial disturbance and add In fingerprint matching data base, add nobody exist in the case of signal characteristic, complete the foundation of fingerprint matching data base;
Omnidirectional personnel detect:
Step 7) utilize the grey relational grade analysis method in statistics to carry out the coupling of signal characteristic, select the degree of association High data field signal feature is as when the signal characteristic of pre-test scene.
So far, it is achieved that the perception of omnidirectional personnel and detection under indoor environment.

Claims (1)

1. personnel's detection method under an indoor environment based on channel condition information, it is characterised in that the method comprises following Concrete steps:
One. channel condition information CSI signal characteristic abstraction and pretreatment:
Step 1) carry out CSI signal data acquisition in indoor environment: platform includes being provided with Ubuntu system, Intel 5300 nothing If one, the mini power control machine of gauze card, virtual CSI and CSI Tool instrument, TP-Link router, external antenna Dry, LCDs, notebook computer are some;In an experiment, TP-Link passes through as signal emitting-source AP, mini power control machine 5300 network interface cards receive wireless signal, and external antenna forms a link as signal receiver MP, every a pair AP, MP;Utilize void Intend status information Virtual CSI and channel condition information instrument CSI Tool gather transmitting channel state information and be saved in In notebook;
Step 2) utilize IFFT Fourier transformation that the frequency-region signal CFR collected is converted into time-domain signal CIR;
Step 3) CIR data message is processed, reduce multipath transmisstion and the impact of shadow fading;
Step 4) the CIR data signal after processing is carried out FFT Fourier transformation, it is reduced into frequency-region signal CFR;
Step 5) utilize the CFR signal data obtained to obtain amplitude and the phase information of channel condition information;
Two. received signals fingerprint Database:
Step 6) for different scenes, gather CSI signal and be trained, choose upper and lower, left and right four direction respectively as entirely To the reference point of simple abstract, carry out the measurement of signal characteristic in the case of artificial disturbance and join fingerprint matching data base In, add nobody exist in the case of signal characteristic, complete the foundation of fingerprint matching data base;
Three. omnidirectional personnel detect:
Step 7) utilize grey relational grade analysis method to carry out the coupling of signal characteristic, according to formula Calculate the degree of association, select data field signal feature corresponding to degree of association maximum as when the signal characteristic of pre-test scene, Wherein X0、XiRepresenting target sequence and comparative sequences respectively, n represents sequence length, γ0iK () is coefficient of association;
So far, it is achieved that the perception of omnidirectional personnel and detection under indoor environment.
CN201610323973.8A 2016-05-16 2016-05-16 Indoor-environment-person detection method based on channel state information Pending CN105873212A (en)

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Cited By (12)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106245619A (en) * 2016-09-14 2016-12-21 中设设计集团股份有限公司 The monitoring system of a kind of ship lock water conservancy project facility structure thing health monitoring and monitoring method
CN106792808A (en) * 2016-12-08 2017-05-31 南京邮电大学 Los path recognition methods under a kind of indoor environment based on channel condition information
CN107036594A (en) * 2017-05-07 2017-08-11 郑州大学 The positioning of intelligent Power Station inspection intelligent body and many granularity environment perception technologies
CN107331136A (en) * 2017-05-11 2017-11-07 深圳市斑点猫信息技术有限公司 Indoor human body activity detection approach and system based on WiFi
CN108038419A (en) * 2017-11-16 2018-05-15 南京邮电大学 Wi-Fi-based indoor personnel passive detection method
CN108337055A (en) * 2018-01-29 2018-07-27 中国矿业大学 A kind of mine disaster monitored by personnel's method based on channel state information
CN108548545A (en) * 2018-06-06 2018-09-18 天津大学 A kind of non-contact more people's step-recording methods and system based on commercial Wi-Fi
CN109587645A (en) * 2018-11-12 2019-04-05 南京邮电大学 Personnel's recognition methods under indoor environment based on channel state information
CN109787695A (en) * 2017-11-13 2019-05-21 戴惠英 Router antenna management system
CN110300399A (en) * 2019-06-24 2019-10-01 西北大学 A kind of short distance multi-user concealed communication method and system based on Wi-Fi network interface card
CN110737201A (en) * 2019-10-11 2020-01-31 珠海格力电器股份有限公司 monitoring method, device, storage medium and air conditioner
WO2024022156A1 (en) * 2022-07-29 2024-02-01 华为技术有限公司 Method for establishing sensing network, and related apparatus

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103596266A (en) * 2013-11-26 2014-02-19 无锡市中安捷联科技有限公司 Method, device and system for detecting and locating human body
CN105979485A (en) * 2016-05-11 2016-09-28 南京邮电大学 Personnel detection method in indoor environment based on channel state information (CSI)

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103596266A (en) * 2013-11-26 2014-02-19 无锡市中安捷联科技有限公司 Method, device and system for detecting and locating human body
CN105979485A (en) * 2016-05-11 2016-09-28 南京邮电大学 Personnel detection method in indoor environment based on channel state information (CSI)

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
FU XIAO等: "《CSAR’15 Proceedings of the 1st Workshop on Context Sensing and Activity Recognition》", 1 November 2015 *
XIONG CAI等: "《2015 International Conference on Wireless Communications & Signal Processing (WCSP)》", 31 December 2015 *

Cited By (14)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106245619A (en) * 2016-09-14 2016-12-21 中设设计集团股份有限公司 The monitoring system of a kind of ship lock water conservancy project facility structure thing health monitoring and monitoring method
CN106792808A (en) * 2016-12-08 2017-05-31 南京邮电大学 Los path recognition methods under a kind of indoor environment based on channel condition information
CN107036594A (en) * 2017-05-07 2017-08-11 郑州大学 The positioning of intelligent Power Station inspection intelligent body and many granularity environment perception technologies
CN107331136A (en) * 2017-05-11 2017-11-07 深圳市斑点猫信息技术有限公司 Indoor human body activity detection approach and system based on WiFi
CN109787695A (en) * 2017-11-13 2019-05-21 戴惠英 Router antenna management system
CN108038419A (en) * 2017-11-16 2018-05-15 南京邮电大学 Wi-Fi-based indoor personnel passive detection method
CN108038419B (en) * 2017-11-16 2022-05-13 南京邮电大学 Wi-Fi-based indoor personnel passive detection method
CN108337055A (en) * 2018-01-29 2018-07-27 中国矿业大学 A kind of mine disaster monitored by personnel's method based on channel state information
CN108548545A (en) * 2018-06-06 2018-09-18 天津大学 A kind of non-contact more people's step-recording methods and system based on commercial Wi-Fi
CN109587645A (en) * 2018-11-12 2019-04-05 南京邮电大学 Personnel's recognition methods under indoor environment based on channel state information
CN110300399A (en) * 2019-06-24 2019-10-01 西北大学 A kind of short distance multi-user concealed communication method and system based on Wi-Fi network interface card
CN110300399B (en) * 2019-06-24 2020-07-21 西北大学 Close-range multi-user covert communication method and system based on Wi-Fi network card
CN110737201A (en) * 2019-10-11 2020-01-31 珠海格力电器股份有限公司 monitoring method, device, storage medium and air conditioner
WO2024022156A1 (en) * 2022-07-29 2024-02-01 华为技术有限公司 Method for establishing sensing network, and related apparatus

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