CN106792808A - Los path recognition methods under a kind of indoor environment based on channel condition information - Google Patents
Los path recognition methods under a kind of indoor environment based on channel condition information Download PDFInfo
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- H—ELECTRICITY
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- H04W84/00—Network topologies
- H04W84/02—Hierarchically pre-organised networks, e.g. paging networks, cellular networks, WLAN [Wireless Local Area Network] or WLL [Wireless Local Loop]
- H04W84/10—Small scale networks; Flat hierarchical networks
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Abstract
The invention discloses a kind of los path recognition methods being applied to based on CSI under indoor environment, in CSI signal acquisition stages, the amplitude information of CSI is extracted from letter bag；Static and two kinds of scenes of dynamic are divided into according to indoor scene；In static scene, extract average, variance, standard deviation, the coefficient of variation, degree of skewness, kurtosis, square, Rician K factors, form feature cluster, then using feature cluster as BP neural network training set, corresponding BP neural network is generated by predefined label, to freshly harvested static point, corresponding feature cluster is directly extracted, input network, realizes static los path identification；In dynamic scene, it is distributed using the Rician based on each sub-carrier amplitude, calculates the average of the Rician K factors of each channel in CSI samples, is carried out dynamic vision and recognized away from obstructed path.The present invention can improve the static accuracy rate recognized with dynamic los path under indoor environment, while the identification of the horizon range in all directions of interior can be realized.
Description
Technical field
Positioning user or the context aware field of terminal the invention belongs to cordless communication network, and in particular to one kind is applicable
In the los path recognition methods under indoor environment, to recognize the los path under indoor dynamic and static state.
Background technology
Currently, with the fast development of wireless communication technology, especially under the promotion of mobile intelligent terminal fashion trend,
WLAN (Wireless Local Area Network, WLAN) becomes to popularize very much, in fields such as family, store, airports
Institute, WLAN signal is seen everywhere.The good WLAN infrastructure of these substantial amounts of installation and deployment, is grinding for WiFi context awares
Study carefully and established physical basis.
Cognition technology based on WiFi signal is mainly including indoor positioning, personnel's detection, action recognition and identification through walls etc..
The cognition technologies based on WLAN existing a large amount of at present are studied, and it is mainly using receiving signal designation strength information (Received
Signal Strength Indicator, RSSI) perceive environmental characteristic realization application.But indoors in environment, RSSI can be because
Small yardstick shadow fading that signal multipath transmisstion causes and no longer increase amplitude under monotone decreasing, and inactive state with propagation distance
Also fluctuation can be produced.Nearest researcher is by changing firmware so that also can be with physical layer channel shape in common WiFi equipment
The form of state information (Channel State Information, CSI) obtains a channel frequency response for sampled version
(Channel Frequency Response,CFR).Compared with RSSI, the RSSI of monodrome is extended to frequency domain by CSI, and attached
Phase information is added, for wireless aware provides more horn of plenty and fine-grained channel condition information from frequency domain.CSI is also caused
Common WiFi equipment can roughly distinguish propagation path from time domain to a certain extent, so as to be answering based on los path
With laying a good foundation.
Using CSI information, WiFi environment perception technologies can realize more accurate indoor positioning, can also realize passive type
Personnel's detection and activity recognition etc..Wherein carry out the realization that sighting distance (Line-Of-Sight, LOS) Path Recognition is function above
Lay a good foundation.At present, the problem that los path identification technology is present is complicated identification process.Such as Publication No.
CN104168650A, it is entitled " based on dynamic radio access point indoor orientation method " disclosure of the invention based on dynamic radio
In the indoor orientation method of access point, be modulated for information after forming modulated signal ripple and passed by wireless channel by node to be positioned
Transport to neighbouring static WAP and dynamic radio access point；Static WAP and dynamic radio access point are according to connecing
The modulated signal ripple for receiving calculates its channel condition information with the wireless channel between node to be positioned, and sends to positioning clothes
Business device；Dynamic radio access point sends to location-server current location information；Location-server is according to WAP
Positional information and the channel condition information for receiving, relative adjacent locations detection, and root are carried out to any two WAP
The band of position of node to be positioned is obtained according to relative adjacent locations testing result utilization space split plot design.The invention can necessarily journey
The indoor positioning problem of degree optimization WLAN, improves positioning precision, eliminates complex indoor environment multipath and non line of sight brings
Position error, but identification process is complicated.In addition, there will be los path recognition methods and be mainly based upon signal receiving strength, but
There is the factors such as environmental disturbances in it, recognize poor robustness.Therefore, it is necessary to the los path identification technology exhibition under indoor environment
Open further investigation, find more preferable los path recognition methods, to obtain more accurately indoor positioning and perceptual performance.
The content of the invention
The technical problem to be solved in the present invention is to provide that a kind of process is simple, strong adaptability, it is adaptable under indoor environment
The recognition methods of los path.
Therefore, the present invention proposes a kind of los path recognition methods suitable under indoor environment, the method is comprising following
Step：
First, CSI signal characteristic abstractions and pretreatment：
Step 1) CSI signal data acquisitions：For the link of specific signal emitting-source AP and signal receiver RT compositions,
Using CSI detection instrument collection transmitting channel state informations, and carry out saving as letter APMB package；
Step 2) above-mentioned letter APMB package is read, extract amplitude and the phase letter of a number of subcarrier of corresponding CSI
Breath；
2nd, static los path identifying schemes：
Step 3) for indoor static scene, the CSI amplitude informations under static scene are gathered first, to being extracted in step 2
Sub-carrier amplitude correlated characteristic, set up feature cluster, feature is including Rician K factors etc.；
Step 4) using the feature cluster of above-mentioned pre-acquired as training set, with reference to predefined label as output, by BP
The algorithm generation network of neutral net；
Step 5) gather the CSI amplitudes of rest point and extract feature, in input neutral net, sighting distance is judged to by output
Scope or non line of sight scope；
3rd, dynamic los path identifying schemes：
Step 6) for indoor dynamic scene, calculated by the LOS/NLOS samples to advance collection, determine reality
When K-Mean threshold values Kth, using based on each sub-carrier amplitude Rician be distributed, calculate each channel in CSI samples
Rician K factors average, then using K-Mean factorization methods, by assuming that the method for inspection is come under realizing dynamic scene
Sighting distance is recognized with obstructed path.
Preferably, the detection instruments of CSI described in step 1 are virtual CSI and CSI Tool.
Certain amount described in step 2 is preferably 30.
Feature cluster described in step 3 also includes average, variance, standard deviation, the coefficient of variation, degree of skewness, kurtosis, square.
Compared with prior art, beneficial effects of the present invention include：
1. simplicity
, using physical layer information CSI as personnel's detection evaluation points under indoor environment, CSI can be from common business for the present invention
Extracted with WiFi equipment, add the widespread deployment of WiFi infrastructure, this causes that obtaining CSI information becomes simply may be used
OK.And not to CSI treatment, using original CSI data, reduce the calculating of correlation and actual expense.
2. adaptability
The present invention replaces traditional RSSI using CSI, overcomes the defect of RSSI coarse sizes and time stability difference, CSI
It is obtained in that more fine-grained information and mulitpath can be distinguished, can be suitably used for more indoor application scenes.
3. feature
It is further contemplated that comprehensive indoor view distance Path Recognition scheme, is quiet by los path identification scene partitioning
Two scenes of state and dynamic, and the different los path recognition methods for every kind of Scenario Design, are compared to traditional
Sighting distance detects that function is more powerful, and effect is more preferable.
4. optimization property
The present invention only only accounts for the amplitude information of CSI signals, by the phase information, the angle that consider combination CSI signals
Information etc. is obtained in that more superior sighting distance Detection results, while being answered for the accuracy such as indoor positioning, gesture identification are higher
With there is provided antecedent condition.
Brief description of the drawings
Fig. 1 is sighting distance and non line of sight schematic diagram under indoor environment.
Fig. 2 is the indoor view distance Path Recognition protocol procedures figure based on CSI.
Specific embodiment
In conjunction with accompanying drawing, specific embodiments of the present invention are further described in detail.
Fig. 1 is the schematic diagram of los path and obstructed path under indoor environment, wherein at RT correspondence its be with AP1 regarding
Away from path, at RT ' places, correspondence and AP1 are obstructed path；Fig. 2 gives the indoor view distance Path Recognition scheme based on CSI
Flow chart.In conjunction with accompanying drawing, specific embodiments of the present invention are further described in detail.The purpose of the present invention is to realize
The identification of the sighting distance and non line of sight in dynamic and static environment indoors.Operation principle is using physical layer channel conditions information
(CSI) traditional reception signal designation strength information (RSSI) is replaced, using CSI information times good stability, to dynamic environment
Interference resistance is strong, the advantage such as strong that there is sensitiveness to link surrounding people, and Intel is based on by being carried under environment indoors
The power control machine of 5300 serial network interface cards, in dynamic with static two kinds of scenes, is respectively adopted the neural net method of different characteristic cluster
With this K factor method of dynamic Lay, realize indoor dynamic and recognized with the accurate los path under static environment.
The present invention is a kind of los path identifying schemes suitable under indoor environment, for interior of the tradition based on RSSI
There is coarse size, time stability difference and cannot distinguish between the defects such as mulitpath information in personnel's detection scheme, the program is utilized
Physical layer channel conditions information (CSI) replaces RSSI, and resistance is disturbed using CSI information times good stability, to dynamic environment
By force, there is the advantages such as sensitiveness is strong to link surrounding people, by being carried under environment indoors based on the serial nets of Intel 5300
The power control machine of card, using neural net method and this K factor method of dynamic Lay of different characteristic cluster, realizes indoor dynamic and static
Accurate los path identification under environment.
This be based on CSI suitable for the los path identifying schemes under indoor environment, be included in step in detail below：
CSI signal characteristic abstractions and pretreatment：
Step 1) CSI signal data acquisitions：Platform include be provided with Ubuntu systems, the wireless network cards of Intel 5300,
One, the mini power control machine of virtual CSI and CSI Tool instruments, TP-Link routers, external antenna are some, liquid crystal
Display screen, notebook computer are some.In an experiment, used as signal emitting-source AP, mini power control machine is connect TP-Link by 5300 network interface cards
Wireless signal is received, external antenna constitutes a link as signal receiver RT, every a pair of AP, RT.Using virtual CSI with
And CSI Tool instruments gather transmitting channel state information and carry out saving as letter APMB package；
Step 2) letter APMB package is read, extract the amplitude and phase information of 30 subcarriers of corresponding CSI；
Static los path identifying schemes：
Step 3) for indoor static scene, the CSI amplitude informations under static scene are gathered first, extract 30 subcarriers
The correlated characteristic of amplitude, sets up feature cluster, feature include average, variance, standard deviation, the coefficient of variation, degree of skewness, kurtosis, square,
Rician K factors；
Step 4) using the feature cluster of pre-acquired as training set, with reference to predefined label as output, by BP nerves
The algorithm of network is come the network that generates；
Step 5) gather the CSI amplitudes of rest point and extract feature, in input neutral net, sighting distance is judged to by output
Scope or non line of sight (NLOS) scope；
Dynamic los path identifying schemes：
Step 6) for indoor dynamic scene, calculated by the LOS/NLOS samples to advance collection, determine reality
When K-Mean threshold values Kth, using based on each sub-carrier amplitude Rician be distributed, calculate each channel in CSI samples
Rician K factors average, then using K-Mean factorization methods, by assuming that the method for inspection is come under realizing dynamic scene
Sighting distance is recognized with obstructed path.
So far, the dynamic and static los path identifying schemes under indoor environment are realized.
For ease of it will be understood by those skilled in the art that technical scheme, now to some involved in above step
Key operation is defined as follows：
Static nature is extracted：
BP neural network algorithm is used in static situation, it is necessary first to which the CSI amplitude informations to gathering are pre-processed,
Correlated characteristic is extracted as training set.Therefore following correlated characteristic is proposed：
Average and variance (μ, σ)：Reflect 30 dispersion degrees of sub-carrier amplitude, usual circumstances in each CSI sample
Under, due to the interference that there is barrier under NLOS, the amplitude between its different sub-carrier can show larger fluctuation, i.e., in NLOS
It is lower to have larger variance and standard deviation next time than LOS.
The coefficient of variation (A):Weigh a statistic of each variable observations degree of variation in data information, A=σ/μ.
Degree of skewness (S)：Quantify deflection characteristics using skewness.Mathematically, degree of skewness S is defined as：
Wherein x, μ, σ are respectively measurement data, average and variance.Under normal circumstances, had under NLOS situations one compared with
Big positive trend.
Kurtosis (κ)：CSI compares with bigger kurtosis in NLOS in LOS situations under usual circumstances, this in order to quantify
Kurtosis, employs kurtosis as candidate feature.Kurtosis κ are defined as：
Square (Bk)：A kind of this K parameter of good Lay estimation, i.e. K2,4.
Wherein B2,B4Respectively second and fourth order central moment of measurement data.Therefore second is also with the addition of with fourth order square
As candidate feature.
Rician K Factor(Kr)：It is defined as the power ratio of main path and dispersion path, Kr=ν2/(2σ2)；Wherein
V represents the main peak of amplitude, and the physical significance of v is the peak value of LOS transmission, and σ represents the variance of amplitude, and σ representatives is that multipath is passed
The intensity of defeated signal, for example, pass through after scattering, reflection and diffraction, reaches the amplitude of the signal of receiving terminal.The Rician-K factors
Bigger, then LOS influence degrees are higher, i.e. multipath effect influence is smaller.
Static los path recognition methods：
Artificial neural network has self study, self-organizing, preferable fault-tolerance and excellent None-linear approximation ability with it.
In actual applications, the artificial nerve network model of 80%-90% uses error back propagation algorithm (BP neural network algorithm), logarithm
According to there is very good effect in classification.The algorithm principle of BP neural network：Estimate output layer using the error after output
The error of conducting shell, recycles this error to estimate the error of more preceding layer before directly, and anti-pass so in layer is gone down, just
Obtain the estimation error of every other layer.For the LOS/NLOS identifications under static state, from BP neural network algorithm to LOS/
NLOS is classified, and detailed process is as follows：
Step 1) sample group of CSI is first gathered, and above-mentioned all features of each CSI sample are calculated, it is special as identification
Levy.For the CSI features gathered under LOS, add and be labeled as 1；Simultaneously for the feature under NLOS, it is -1 that bidding is signed.Will be all
Feature samples as training set be input into, its corresponding tally set as training set export, train the BP neural network.
Step 2) setting of BP network parameters, in order to simplify the computing of BP neural network, employ the BP nerves of single hidden layer
Network, its hidden layer node sets number and meets, empirical equationWherein m, n are respectively input layer, output layer
Node number, α is the constant between 1~10.
Step 3) characteristic value of CSI is resurveyed, as test set.And it is input to the BP neural network for having trained
In, the output of network is obtained, the output of network is contrasted with predefined test set label.
Step 4) because BP networks have error when in use, output sample will not be predefined 1 or -1, therefore right
LOS identifying schemes checked using traditional binary system, LOS situations are H0, NLOS situations are H1。
For the sample for exporting, it is assumed that verify as:
Wherein label_output is exported for the sample of BP neural network.
Dynamic los path recognition methods：
When target is kept in motion, influence can be produced on the sample of CSI, can not be reached using above-mentioned static feature
To good recognition effect.Because the current CSI for using can expose more fine-grained channel information, each CSI sample bag
Containing 30 information of subcarrier, so using the distributed model for carrying out to each subcarrier Rician K, so as to propose K-Mean
Feature.
K-Mean：First in the CSI data sets of collection, the Rician K factors of its corresponding each subcarrier are calculated,
Its mathematical notation is：
viRepresent i-th amplitude peak of subcarrier, σiRepresent that the amplitude standards of i-th subcarrier are poor.On this basis,
By calculating the average of all subcarriers of each CSI sample in real time, dynamic is realized as a label at current time
The identification of LOS and NLOS under scene.
Wherein Rician-KiIt is the Rician K factors of i-th subcarrier mentioned above.
Calculated by the LOS/NLOS samples to advance collection, determine real-time K-Mean threshold values Kth, it is corresponding
The method that LOS identifying schemes are checked using hypothesis below：
Wherein H0It is LOS situations, H1It is NLOS situations, K-Mean is the real-time Rician K averages of collection.
This be based on channel condition information suitable for the los path identifying schemes under indoor environment, be included in detail below
In step：
CSI signal characteristic abstractions and pretreatment：
Step 1) CSI signal data acquisitions：Platform include be provided with Ubuntu systems, the wireless network cards of Intel 5300,
One, the mini power control machine of virtual CSI and CSI Tool instruments, TP-Link routers, external antenna are some, liquid crystal
Display screen, notebook computer are some.In an experiment, used as signal emitting-source AP, mini power control machine is connect TP-Link by 5300 network interface cards
Wireless signal is received, external antenna constitutes a link as signal receiver RT, every a pair of AP, RT.Using virtual CSI with
And CSI Tool instruments gather transmitting channel state information and carry out saving as letter APMB package；
Step 2) letter APMB package is read, extract the amplitude and phase information of 30 subcarriers of corresponding CSI；
Static los path identifying schemes：
Step 3) for indoor static scene, the CSI amplitude informations under static scene are gathered first, extract 30 subcarriers
The correlated characteristic of amplitude, sets up feature cluster, feature include average, variance, standard deviation, the coefficient of variation, degree of skewness, kurtosis, square,
Rician K factors；
Step 4) using the feature cluster of pre-acquired as training set, with reference to predefined label as output, by BP nerves
The algorithm of network is come the network that generates；
Step 5) gather the CSI amplitudes of rest point and extract feature, in input neutral net, sighting distance is judged to by output
Scope or non line of sight (NLOS) scope；
Dynamic los path identifying schemes：
Step 6) for indoor dynamic scene, calculated by the LOS/NLOS samples to advance collection, determine reality
When K-Mean threshold values Kth, using based on each sub-carrier amplitude Rician be distributed, calculate each channel in CSI samples
Rician K factors average, then using K-Mean factorization methods, by assuming that the method for inspection is come under realizing dynamic scene
Sighting distance is recognized with obstructed path.
So far, the dynamic and static los path identifying schemes under indoor environment are realized.
Claims (4)
1. a kind of los path recognition methods suitable under indoor environment, it is characterised in that the method includes the steps of：
First, CSI signal characteristic abstractions and pretreatment：
Step 1) CSI signal data acquisitions：For the link of specific signal emitting-source AP and signal receiver RT compositions, utilize
CSI detection instrument collection transmitting channel state informations, and carry out saving as letter APMB package；
Step 2) above-mentioned letter APMB package is read, extract the amplitude and phase information of a number of subcarrier of corresponding CSI；
2nd, static los path identifying schemes：
Step 3) for indoor static scene, the CSI amplitude informations under static scene are gathered first, to the son extracted in step 2
The correlated characteristic of carrier amplitude, sets up feature cluster, and feature is including Rician K factors etc.；
Step 4) using the feature cluster of above-mentioned pre-acquired as training set, with reference to predefined label as output, by BP nerves
The algorithm generation network of network；
Step 5) gather the CSI amplitudes of rest point and extract feature, in input neutral net, horizon range is judged to by output
Or non line of sight scope；
3rd, dynamic los path identifying schemes：
Step 6) for indoor dynamic scene, calculated by the LOS/NLOS samples to advance collection, determine in real time
K-Mean threshold values Kth, are distributed using the Rician based on each sub-carrier amplitude, calculate each channel in CSI samples
The average of Rician K factors, then using K-Mean factorization methods, by assuming that the method for inspection is realized being regarded under dynamic scene
Recognized away from obstructed path.
2. a kind of los path recognition methods suitable under indoor environment as claimed in claim 1, it is characterised in that step 1
Described in CSI detection instrument be virtual CSI and CSI Tool.
3. a kind of los path recognition methods suitable under indoor environment as claimed in claim 1, it is characterised in that step 2
Described in certain amount be 30.
4. a kind of los path recognition methods suitable under indoor environment as claimed in claim 1, it is characterised in that step 3
Described in feature cluster also include average, variance, standard deviation, the coefficient of variation, degree of skewness, kurtosis, square.
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