CN107451605A - A kind of simple target recognition methods based on channel condition information and SVMs - Google Patents

A kind of simple target recognition methods based on channel condition information and SVMs Download PDF

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CN107451605A
CN107451605A CN201710568384.0A CN201710568384A CN107451605A CN 107451605 A CN107451605 A CN 107451605A CN 201710568384 A CN201710568384 A CN 201710568384A CN 107451605 A CN107451605 A CN 107451605A
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csi
data
target
svm
simple target
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周瑞
陈结松
鲁翔
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University of Electronic Science and Technology of China
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    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
    • G06F18/2411—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on the proximity to a decision surface, e.g. support vector machines
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    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B17/00—Monitoring; Testing
    • H04B17/30—Monitoring; Testing of propagation channels
    • H04B17/309—Measuring or estimating channel quality parameters
    • H04B17/345—Interference values

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Abstract

A kind of simple target recognition methods based on channel condition information and SVMs proposed by the present invention, it is not necessary to build special hardware facility, make full use of prior wireless network, the function of simple target identification is reached using common commercial router can.After CSI initial data is obtained, use density-based algorithms DBSCAN to cluster the subcarrier data in channel first with denoising, then the data after denoising are carried out using the moving average algorithm based on weights smooth.After data prediction, the present invention carries out characteristics extraction using Principal Component Analysis Algorithm to data.Data after pretreatment and feature extraction can more accurately reflect the Main change of signal and dimension substantially reduces, and are favorably improved target identification precision and reduce computation complexity.The present invention is by means of the SVM multi-classification algorithms based on one against one strategies, the statistical model of non-linear dependence between acquisition destination object and received signals fingerprint, so as to reach the purpose of simple target identification.

Description

A kind of simple target recognition methods based on channel condition information and SVMs
Technical field
The present invention relates to field of target recognition, more particularly to one kind based on channel condition information and to use SVMs skill Art carries out simple target and knows method for distinguishing.
Background technology
WLAN based on Wi-Fi obtains widespread deployment indoors, while data transport service is provided, may be used also Simple target identification service is provided.The proportion that water in human body accounts for is 70 percent, and water is to have very to radio frequency signal Strong absorbability, so people knows from experience produces reflection, scattering, diffraction, decay and other effects to the Wi-Fi signal of surrounding, pass through The special fingerprint characteristic that monitoring human body can be formed to the interference caused by Wi-Fi signal, whether can be that people carries out letter to target Single target identification.
Received signal strength indicator (Received Signal Strength can be obtained from Wi-Fi signal Indicator, RSSI) and channel condition information (Channel State Information, CSI).RSSI is current using most Extensive energy response, but the accurate perception under its coarseness and variable sexual incompatibility multi-path indoor environment, for target identification Precision is very poor.CSI is physical layer attributes, the decay factor that description signal is propagated between transmitter and receiver, including scattering, The information such as environmental attenuation, range attenuation, the interference of the narrow-band signal from frequency range can be resisted, it is steady enough in a static environment It is fixed, it can be made a response immediately when disturbed, and the signal from mulitpath can be differentiated, multipath effect influences small.With just Handing over frequency division multiplexing (Orthogonal Frequency Division Multiplexing, OFDM), technology is in a wireless local area network Application, can more easily obtain CSI at present.CSI includes the amplitude and phase information of each subcarrier, using the teaching of the invention it is possible to provide abundant Frequency domain information, so as to improve the accuracy of target identification.
On the process nature of simple target identification and a process classified, interference of the CSI signals for human body show Substantially, there is good identification, it is possible to solve the problem of simple target identifies using the thought of classification.SVMs (Support Vector Machines, SVM) is a kind of machine learning method based on Statistical Learning Theory, is solving higher-dimension Many advantages are shown with terms of nonlinear problem, by means of svm classifier, can be obtained non-between identification target and received signals fingerprint The statistical model of linear dependence.
The content of the invention
The present invention is a kind of simple target identification classified based on channel condition information (CSI) and SVMs (SVM) Method, comprise the following steps：
Step 1：Environment is disposed, and the simple target identification based on Wi-Fi requires in-door covering Wi-Fi signal, selection signal Less 5G frequency ranges are disturbed, equipment is two notebook computers, is respectively arranged with Intel link 5300agn wireless business network interface cards；
Step 2：CSI raw data acquisitions, some CSI initial data of different target object are gathered, including：Send day Line number, reception antenna number, transmission frequency, channel condition information CSI matrixes；
Step 3：CSI data predictions, including：(1) dimension of CSI matrixes first in initial data is removed, will be produced Two-dimensional matrix be transformed into logarithm (power) space from linear (level) space, by each complex conversion in matrix into value； (2) there are 30 subcarriers in every a pair channels for sending and receiving antenna composition, using density-based algorithms Density-Based Spatial Clustering of Applications with Noise (DBSCAN) are believed each Road is clustered, by deleting outlier denoising；(3) using the moving average algorithm based on weights to the CSI data after denoising Carry out smooth；
Step 4：CSI characteristics extractions, use principal component analysis (Principal Component Analysis, PCA) Algorithm carries out dimensionality reduction and characteristics extraction to pretreated CSI data, produces CSI sample fingerprints；
Step 5：SVM model trainings；Step is as follows：(1) CSI sample fingerprints are normalized；(2) CSI fingerprint samples are based on This, establishes the more disaggregated models of SVM, the corresponding target of each classification；
Step 6：Simple target identifies that step is as follows：(1) CSI raw data acquisitions are carried out according to step 2；(2) according to Step 3 carries out CSI data predictions；(3) CSI Data Dimensionality Reductions and characteristics extraction are carried out according to step 4, obtains live signal Fingerprint；(4) determine the CSI fingerprints measured in real time according to the voting results of the more disaggregated models of SVM representated by target, realize letter Single goal identifies；
Brief description of the drawings
Fig. 1 is the simple target recognition methods flow chart based on CSI and SVM；
Fig. 2 is the environment deployment diagram of the simple target identification based on CSI；
Embodiment
The embodiment of the present invention is described below in conjunction with the accompanying drawings, so as to those skilled in the art preferably Understand the present invention.Requiring particular attention is that in the following description, may desalination and ignore it is relevant with the present invention Know the content introduction of function and design.
In the present embodiment, the present invention mainly includes following link to simple target recognition methods：Data acquisition, data Pretreatment, the feature extraction of data, simple target Classification and Identification, flow chart is as shown in figure 1, specific implementation step is as follows：
Step 1：Environment is disposed, and the simple target identification based on Wi-Fi requires in-door covering Wi-Fi signal, experiment scene A length of 6 meters of layout, a width of 7 meters, the less 5G frequency ranges of Systematic selection signal interference, equipment is two notebook computers, model It is the THINKPAD of association T400 and X201i respectively, they are equipped with intel link 5300agn wireless business network interface cards, should Network interface card has 3 antennas.T400 sets directive sending to broadcast bag data, the equipment as signal is sent, and X201i is set to monitor mould Formula, the equipment as signal is received, layout type schematic diagram is referring to Fig. 2.
Step 2：CSI raw data acquisitions, the training stage, 4 kinds of scenes will be divided into gather the sample of training data, point It is not dummy, male laboratory technician and the female laboratory technician that nobody, foam are done.Make target static per second in position, X201i shown in Fig. 2 20 CSI initial data from T400 are gathered, including：Transmission antenna number Ntx, reception antenna number Nrx, packet transmission Frequency f, channel state information matrix H.Channel condition information H is a Ntx×Nrx× 30 three-dimensional matrice, the third dimension are OFDM 30 subcarriers information h=in channel | h | ejsinθ, | h | it is subcarrier amplitude, θ is sub-carrier phase.
Step 3：CSI data generate, and for the CSI initial data of collection, remove CSI matrix Hs first first ties up Degree, obtain NtxIndividual Nrx× 30 two-dimensional matrix, two-dimensional matrix is converted into logarithm (power) space from linear (level) space, And by each complex conversion in matrix into value.
Step 4：CSI data de-noisings, send and receive antenna for every a pair and form a channel, therefore a pair of AP-MP are included Ntx×NrxBar channel；Every channel includes 30 subcarriers, therefore a pair of AP-MP include Ntx×Nrx× 30 subcarriers.According to CSI data sets are divided into N by channeltx×NrxIndividual Sub Data Set, each Sub Data Set include 30 subcarriers information.Make subdata Serial number index (1~30), the amplitude value per subcarriers are concentrated, is applied on each Sub Data Set based on density Clustering algorithm DBSCAN is clustered.Two parameters in DBSCAN are that field radius e and minimum include points minOpt respectively, Its sorting procedure includes：(1) it is non-access state, i.e. " unvisited " by all object tags in Sub Data Set；(2) it is random Selection one does not access object o (index, value), labeled as " visited "；Check whether o neighborhood comprises at least MinOpt object：If it is not, then mark o is outlier；If it is, create a new cluster C and a Candidate Set for o N is closed, all objects in o neighborhood are placed in candidate collection N；(3) DBSCAN is iteratively the object that other clusters are not belonging in N It is added in C, until N is sky, cluster C is completed；(4) go to step (2) and handle next object；(5) outlier will be marked as Sample data is deleted from training set corresponding to object, reaches the purpose of data de-noising.
Step 5：CSI data smoothings, the CSI data after denoising are put down using the moving average algorithm based on weights Slide to reduce data fluctuations.Assuming that a CSI subcarriers sequence to moment t is (v1, v2,…,vt), then in moment t CSI Smooth valueIt is the weighted average of preceding m value, m is sliding window size：
Step 6：CSI characteristics extractions, (1) assume N be presentapAP-MP is combined, each pair AP-MP includes Ntx×NrxBar Channel, every channel have 30 subcarriers, then the dimension of every CSI data is Nap×Ntx×Nrx× 30, dimension is very high.CSI Each contribution of the value to classification is different in data, therefore carries out dimensionality reduction simultaneously to CSI data using principal component analysis PCA algorithms Extract maximally effective feature.PCA target is to find r (r<Nap×Ntx×Nrx× 30) individual new feature, each new feature are original The linear combination of feature, new feature can reflect the principal character of legacy data, and can compress the scale of legacy data.(2) it is false If training sample set matrix is X, sample size N.By PCA, transition matrix C can be obtained, sample matrix X is by conversion Matrix S afterwards, and the feature weight L=(L arranged in descending order1,L2,…,Ln), n is characterized dimension.Calculate feature LrAccumulation Contribution rate is：
If Lr>95%, i.e., the accumulation contribution rate of preceding r feature is more than predetermined threshold value 95%, then takes L=(L1,L2,…,Ln) in Preceding r feature F=(L1,L2,…,Lr) as the feature extracted.Preceding r row in matrix S form principal component matrix R.R conducts The sample data of following model training.
Step 7：CSI sample fingerprints generate and normalization, it is assumed that the CSI sample sets of detection zone are X={ c1, c2,…,cN, N is number of samples；ci={ ci1,ci2,…,cirThe sample after feature extraction is represented, r is sample dimension, normalizing Change obtains：
cijRepresent sample ciJ-th of characteristic value, cminRepresent the minimum value of all characteristic values, cmaxRepresent all characteristic values Maximum；
Step 8：Svm classifier model training, svm classifier model training is carried out to each target.Due to multiple identification mesh be present Mark, therefore multiple target classification is assorting process more than one, is expanded to SVM from two classification using one-against-one strategies More classification, finally carry out ballot and obtain final classification result.Assuming that destination number is K, the corresponding class of each target, then
For the quantity of class for K, it is necessary to train K (K-1)/2 grader, each grader carries out two classification to two targets.Assuming that ωiAnd ωjTarget d is represented respectivelyiWith target djCorresponding class, then for class ωiWith class ωjThe training of SVM classifier be exactly Using from ωiAnd ωjIn CSI sample fingerprints solve following problem：
Wherein C is constant, and ξ is one group of slack variable, ctIt is a CSI sample fingerprint, N is number of samples.
Step 9：Online target identification, when realistic objective identifies, algorithm passes through training according to the CSI fingerprints gathered in real time Obtained SVM classifier determines the classification of target.Comprise the following steps：(1) mode of step 2 gathers CSI initial data；(2) CSI data are pre-processed in the way of step 3 to step 6 and feature extraction；(3) produced in the way of step 7 CSI fingerprints simultaneously normalize；(4) representated by determining the CSI fingerprints measured in real time according to the voting results of multiple svm classifier models Target.
The beneficial effects of the invention are as follows：Simple target identification solution based on CSI need not build special hardware Facility, prior wireless network is made full use of, the function of simple target identification is reached using common commercial router can.Obtaining After taking CSI initial data, the present invention pre-processes to data, first using density-based algorithms DBSCAN to channel In subcarrier data clustered with denoising, then the data after denoising are carried out using the moving average algorithm based on weights Smoothly.After data prediction, the present invention carries out characteristics extraction using Principal Component Analysis Algorithm to data.Pretreatment and feature carry Data after taking can more accurately reflect the Main change of signal and dimension substantially reduces, and are favorably improved target identification Precision simultaneously reduces computation complexity.The present invention is obtained by means of the SVM multi-classification algorithms based on one-against-one strategies The statistical model of non-linear dependence between destination object and received signals fingerprint, so as to reach the purpose of simple target identification.This Invention can reach the precision of more than 98.7% target identification.
Although the illustrative embodiment of the present invention is described above, in order to the technology people of this technology neck Member understands the present invention, it should be apparent that the invention is not restricted to the scope of embodiment, to the ordinary skill of the art For personnel, as long as various change, in the spirit and scope of the present invention that appended claim limits and determines, these become Change is it will be apparent that all utilize the innovation and creation of present inventive concept in the row of protection.

Claims (2)

1. the present invention is a kind of simple target identification side classified based on channel condition information (CSI) and SVMs (SVM) Method, mainly including herein below：The collection and pretreatment of CSI data, CSI data characteristicses are extracted, to extracting feature off-line training Multiple SVM models, simple target ONLINE RECOGNITION stage.
Technical scheme is as follows：
Step 1：Simple target identification based on Wi-Fi requires in-door covering Wi-Fi signal, and selection signal disturbs less 5G Frequency range, equipment are two notebook computers, are respectively arranged with Intel link 5300agn wireless business network interface cards；
Step 2：Some CSI initial data of different target object are gathered, including：Transmission antenna number, reception antenna number, Transmission frequency, channel condition information CSI matrixes；
Step 3：CSI data predictions, including：(1) dimension of CSI matrixes first in initial data is removed, two by caused by Dimension matrix is transformed into logarithm (power) space from linear (level) space, by each complex conversion in matrix into value；(2) it is every There are 30 subcarriers in a pair of channels for sending and receiving antenna composition, using density-based algorithms Density- Based Spatial Clustering of Applications with Noise (DBSCAN) gather to each channel Class, by deleting outlier denoising；(3) the CSI data after denoising are carried out using the moving average algorithm based on weights smooth；
Step 4：CSI characteristics extractions, using principal component analysis (PCA) algorithm to pretreated CSI data carry out dimensionality reduction and Characteristics extraction, produce CSI sample fingerprints；
Step 5：SVM model trainings, step are as follows：(1) CSI sample fingerprints are normalized；(2) CSI sample fingerprints are based on, are built The more disaggregated models of vertical SVM, the corresponding target of each classification；
Step 6：Simple target identifies that step is as follows：(1) CSI raw data acquisitions are carried out according to step 2；(2) according to step Three carry out CSI data predictions；(3) CSI Data Dimensionality Reductions and characteristics extraction are carried out according to step 4, obtains live signal and refer to Line；(4) determine the CSI fingerprints measured in real time according to the voting results of the more disaggregated models of SVM representated by target.
A kind of 2. simple target classified based on channel condition information CSI and support vector machines according to claim 1 Recognition methods, its feature are (1) data prediction, and each channel is entered using density-based algorithms (DBSCAN) Row cluster, deletes outlier denoising；(2) CSI Data Dimensionality Reductions and characteristics extraction, using principal component analysis (PCA) algorithm to pre- CSI data after processing carry out dimensionality reduction and obtain CSI sample fingerprints；(3) by means of the SVM based on one-against-one strategies Multi-classification algorithm, the statistical model of non-linear dependence between target and received signals fingerprint is obtained, reach the purpose of target identification.
CN201710568384.0A 2017-07-13 2017-07-13 A kind of simple target recognition methods based on channel condition information and SVMs Pending CN107451605A (en)

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Cited By (5)

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CN109061600A (en) * 2018-09-28 2018-12-21 上海市刑事科学技术研究院 A kind of target identification method based on millimetre-wave radar data
CN109657572A (en) * 2018-12-04 2019-04-19 重庆邮电大学 Goal behavior recognition methods after a kind of wall based on Wi-Fi
CN110730473A (en) * 2019-09-03 2020-01-24 中国人民解放军陆军工程大学 Signal feature extraction method for WiFi activity recognition
CN111262637A (en) * 2020-01-15 2020-06-09 湖南工商大学 Human body behavior identification method based on Wi-Fi channel state information CSI
CN111836278A (en) * 2019-08-09 2020-10-27 维沃移动通信有限公司 Measurement reporting and processing method, equipment and medium in secondary cell activation

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Cited By (7)

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Publication number Priority date Publication date Assignee Title
CN109061600A (en) * 2018-09-28 2018-12-21 上海市刑事科学技术研究院 A kind of target identification method based on millimetre-wave radar data
CN109657572A (en) * 2018-12-04 2019-04-19 重庆邮电大学 Goal behavior recognition methods after a kind of wall based on Wi-Fi
CN111836278A (en) * 2019-08-09 2020-10-27 维沃移动通信有限公司 Measurement reporting and processing method, equipment and medium in secondary cell activation
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CN111262637B (en) * 2020-01-15 2021-09-28 湖南工商大学 Human body behavior identification method based on Wi-Fi channel state information CSI

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