CN104185275B - A kind of indoor orientation method based on WLAN - Google Patents

A kind of indoor orientation method based on WLAN Download PDF

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CN104185275B
CN104185275B CN201410458932.0A CN201410458932A CN104185275B CN 104185275 B CN104185275 B CN 104185275B CN 201410458932 A CN201410458932 A CN 201410458932A CN 104185275 B CN104185275 B CN 104185275B
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vector
rssi
region
characteristic
data
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CN201410458932.0A
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CN104185275A (en
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诸彤宇
刘帅
宋志新
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北京航空航天大学
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Abstract

The invention discloses a kind of indoor orientation method based on WLAN, belong to indoor wireless communication and network technique field.Method includes:The RSSI data predictions for each AP that sampled point is collected, a peacekeeping bivector is therefrom extracted respectively as characteristic vector;Feature vector clusters are analyzed area to be targeted is divided into multiple positioning subregions;For every group of characteristic vector, respective disaggregated model is respectively trained out;Poll highest subregion set is chosen from all subregions with reference to " ballot " mechanism based on disaggregated model;Positioned using two-wheeled and reduce subregion range of convergence, improve positioning precision.Fully digging utilization of the invention RSSI spatial distribution characteristic, solves the problems such as large-scale indoor positioning search package space is excessive, and computation complexity is high;New location model is established, is solved in existing WLAN indoor orientation methods, the problems such as can not effectively learning and adapt to RSSI signals non-linear caused by the reason such as non line of sight transmission effects, RSSI attenuation laws be abnormal, non-gaussian statistical property.

Description

A kind of indoor orientation method based on WLAN
Technical field
The present invention relates to a kind of localization method in WLAN indoor positionings field, belongs to indoor wireless communication and network technology neck Domain.
Background technology
In recent years, as the continuous improvement of people's living standard, people are also growing day by day to the demand of location-based service, Such as in personal scheduling, asset management, emergency relief, security monitoring, sacurity dispatching, intelligent transportation, digital map navigation, travel guide All many-sided widespread demands to positioning;It is that such as emergency relief, disaster relief emergency command are dispatched particularly in reply emergency Under special applications scene, location information is more particularly important.
With the further investigation of general fit calculation machine and Distributed Communication Technology, indoor wireless communication and network technology are rapidly sent out Exhibition, has derived and has been based on WLAN (Wireless Local Area Networks, WLAN), Bluetooth (bluetooth), The indoor positioning modes such as WSN (wireless sensor network, wireless sensor network), and based on fingerprint and probability The indoor orientation method of method.
Based on WLAN, Bluetooth, WSN etc. location technology, by carrying out mesh generation, and portion indoors indoors Affix one's name to substantial amounts of AP (Access Point, access points), the RSSI for multiple AP that terminal detection receives in each grid (Received Signal Strength Indication, received signal strength indicator), due to diverse location receive it is each The signal intensity that individual signal node is sent is different, using the RSSI of each node received in each grid as the network Characteristic quantity to complete to position.
Indoor positioning based on fingerprint, by gathering the RSSI of different AP in room area, and by corresponding wireless access The address and coordinate of point are stored in database, and terminal user measures surrounding wireless signal strength, by it and are stored in advance in number Matching positioning is carried out in right amount according to the RSSI in storehouse, so as to obtain being positioned the coordinate information of terminal user.
Probabilistic method draws the RSSI signal probabilities distribution in each reference point using the existing training sample in reference point. Probability Distribution Fitting is typically carried out using Gaussian function, draws the average and bandwidth of the gaussian probability distribution of each reference point.Generally Rate method takes full advantage of the statistical nature of signal distributions, and positioning precision typically will height compared with weighted nearest neighbor method.
However, they equally exist the problem of respective.Indoor orientation method based on fingerprint, in actual applications, for Large-scale indoor positioning, Existential Space matching hunting zone is larger, and computation complexity is high, and memory space requirement is larger not , RSSI signals in actual applications be present in the reference point that some is fixed in foot, and the indoor orientation method based on probabilistic method Non-gaussian, non-linear, multi-modal characteristic is presented in probability distribution so that the probability-distribution function and actual probability distribution fitted Differ larger, so as to cause positioning when larger matching error.
The content of the invention
The technical problem to be solved in the present invention is:What deficiency of prior art overcome, there is provided a kind of room based on WLAN Interior localization method, matching hunting zone can be reduced, the forecast model that and can is tallied with the actual situation, is reduced to a certain extent Computation complexity and time complexity.
The technical problem to be solved in the present invention is:Matching hunting zone is reduced, establishes a kind of prediction to tally with the actual situation Model, there is provided a kind of indoor orientation method based on WLAN, comprise the following steps realization:
Step 1:The RSSI data predictions for each AP that sampled point is collected, therefrom extract a peacekeeping two dimension to Amount is respectively as characteristic vector.
Carrying out necessary pretreatment to the RSSI data scanned includes:The data that RSSI is less than -100dB are deleted, are deleted Non-locating AP data.The data for deleting non-locating AP refer to, delete the RSSI for the AP for being unsuitable for positioning.It is unsuitable for positioning AP feature is intensity too low (RSSI is less than -95dB) or less stable (variance is more than 20), can increase meter using these AP Complexity is calculated, reduces positioning precision, therefore excluded.
Extracted using Different Extraction Method from initial data it is a variety of can the accurate quantification RSSI regularities of distribution characteristic vector. Comprise the following steps:
(1) by all AP scanned according to MAC Address ascending sort, all original numbers that will be scanned when gathering offline Corresponding sampling point number on position mark is gathered according to according to it;
(2) respective characteristic vector can be extracted according to following two methods:
A. by the AP combination of two after sequence, i.e., AP is divided into according to MAC AddressGroup, every group of AP are expressed as (APi, APj) (wherein, 0<i<J≤m, m represent all AP number), corresponding A P is extracted from the initial data for marked sampled point The RSSI of combination is vectorial and corresponding sampled point;
B. each AP is separately as one group, will all offline gathered datas be divided into m groups according to AP MAC Address, every group AP is expressed as APi(wherein, 0<I≤m, m represent all AP number), extracted from the initial data for marked sampled point pair Answer AP RSSI one-dimensional vectors and corresponding sampled point.
Step 2:Feature vector clusters are analyzed, area to be targeted is divided into multiple positioning subregions, per sub-regions Reflect a kind of RSSI distribution characteristics.
Using the characteristic vector constructed in step 1 as input, measuring similarity function is used as using the distance between characteristic vector Carry out cluster analysis.Optionally, cluster analysis is using the X-means algorithms that can find clusters number automatically.X-means clusters are calculated Method improves K-means algorithms, and cluster numbers K need not be preassigned in the initial computing of algorithm, need to only specify K value model Enclose [K1, K2] (K1<K2), algorithm will find an optimal cluster numbers K in specified scope, realize clustering.X- For means algorithms using bayesian information criterion as guidance, the cluster centre for constantly traveling through inhomogeneity cluster is to represent different signal spies Sign, signal characteristic reflect the clustering phenomena of the signal distributions in a certain region.
Step 3:For every group of characteristic vector combination cluster result, respective disaggregated model is respectively trained out;It is based on Disaggregated model chooses poll highest subregion set with reference to " ballot " mechanism from all subregions.Including:
Off-line phase, the characteristic vector constructed for the two kinds of structures method proposed in step 2, it is respectively trained out every SVMs (Support Vector Machine, SVM) classification mould corresponding to every feature vectors of kind building method Type.SVM is built upon VC dimensions (VC dimension) theory and structural risk minimization (structural of statistical learning Risk minimization) on principle basis.SVM by nicety of grading (to the classification correctness of specific sample) and point Class ability (carrying out error-free misclassification to arbitrary sample) trades off, to make grader obtain best Generalization Ability.Feature It is worth the input as SVM classifier, is the abstractdesription to data, therefore the selection of characteristic value is extremely important, can be accurate Reflect that data characteristicses to be sorted will directly affect final classifying quality.
On-line stage, from real time data extraction characteristic of division vector, read the corresponding svm classifier that off-line phase trains Model, according to the supporting vector polynomial expansion entry value, the probability that vector to be sorted corresponds to different zones is calculated, with reference to " ballot " mechanism chooses poll highest set of regions R from all areas.
The concrete operations in tuning on-line stage include:
(1) the svm classifier model trained is read, calculates supporting vector polynomial expansion entry value;
(2) RSSI currently collected, extraction characteristic of division vector are read;
(3) by Polynomial kernel function by characteristic of division DUAL PROBLEMS OF VECTOR MAPPING to higher dimensional space, it is and more according to the supporting vector Item formula expansion entry value calculates the probability that vector to be sorted corresponds to different zones;
(4) for each AP groups (APi,APj), judge whether every sub-regions for marking off are eligible, if there is more Sub-regions are eligible, then SVM models think that current device is likely to be in the union of these subregions;
The qualified region refers to, as AP groups (APi,APj) in the prediction probability of a certain subregion it is more than a certain threshold Value ε (0<ε<1) when, it is qualified to be considered as the region;
(5) poll highest set of regions R is chosen from all areas with reference to " ballot " mechanism, specific steps include:
If AP groups (APi,APj) sample data be identified as by SVM predictions in a certain region, then the region ticket Number plus 1.From geometrically showing as choosing localization region of the most region of capped number as coarseness, the ticket in each region Number should arrive 0Between.
Step 4:Positioned using two-wheeled and reduce set of regions scope, improve positioning precision.Specifically include:
(1) the svm classifier model trained is read, calculates supporting vector polynomial expansion entry value;
(2) RSSI currently collected, extraction characteristic of division vector are read, and characteristic of division is standardized;
(3) by Polynomial kernel function by characteristic of division DUAL PROBLEMS OF VECTOR MAPPING to higher dimensional space, it is and more according to the supporting vector Item formula expansion entry value calculates the probability that vector to be sorted corresponds to different zones, and the coarseness therefrom obtained in selecting step three is determined The probability of regional in the region R of position;
(4) for each APi, judge whether every sub-regions for marking off are eligible, and the subregion is step 3 In the coarseness localization region R subset obtained, eligible if there is more sub-regions, then SVM models are thought currently to set It is standby to be likely to be in the union of these subregions;
The qualified region refers to, works as APiIt is more than a certain threshold epsilon (0 in some region of prediction probability<ε<1) When, it is qualified to be considered as the region;
(5) poll highest set of regions R ' is chosen from R with reference to " ballot " mechanism, specific steps include:If APiSample Notebook data is identified as in a certain region by SVM predictions, then the region poll adds 1.It is coated to from geometrically showing as choosing As fine-grained localization region, the poll in each region should be 0 between m in lid number most region.
The beneficial effect of technical scheme provided by the invention is:Of the invention fully digging utilization RSSI spatial distribution is special Sign, reduces the localization region deviation caused by region division is improper;New location model is established, it is fixed in existing WLAN rooms to solve Position method in, can not effectively learn and adapt to RSSI signals because non line of sight transmission effects, multipath transmisstion effect and RSSI decline Subtract non-linear, the non-gaussian statistical properties caused by reason such as rule exception, and large-scale indoor positioning, search for package space It is excessive, the problems such as computation complexity is high.
Brief description of the drawings
Technical scheme in order to illustrate the embodiments of the present invention more clearly, make required in being described below to embodiment Accompanying drawing is briefly described, it should be apparent that, drawings in the following description are only some embodiments of the present invention, for For those of ordinary skill in the art, on the premise of not paying creative work, other can also be obtained according to these accompanying drawings Accompanying drawing.
Fig. 1 is the inventive method implementation process figure;
Fig. 2 is the cluster flow chart of the inventive method;
Fig. 3 is another cluster flow chart of the inventive method;
Fig. 4 is the training flow chart of the inventive method;
Fig. 5 is a kind of coarseness positioning flow figure of the inventive method;
Fig. 6 is a kind of fine granularity positioning flow figure of the inventive method.
Embodiment
Specific embodiments of the present invention are described further with reference to flow chart and specific embodiment.
Fig. 2 is the cluster flow chart of the inventive method, and the flow belongs to a part for off-line phase.It can specifically include such as Lower step:
201st, smart mobile phone high frequency sweep periphery AP signals, the data format scanned such as table 1 are used in each school punctuate It is shown.It should be noted that the number of data of each school punctuate collection is not fixed, it is different because of acquisition time length.It is if current Position fails to collect corresponding AP RSSI, is filled up with -100dB.
The scan data form of table 1
School punctuate numbering AP1 AP2 AP3 AP4 AP5 AP6 AP7
1 -85 -97 -63 -100 -100 -90 -72
1 -83 -92 -65 -100 -98 -85 -69
2 -70 -73 -95 -82 -63 -100 -100
…… …… …… …… …… …… …… ……
202nd, from all AP of extracting data of collection, according to MAC Address ascending sort.It is intended that positioning stage The SVM algorithm used and vector are order dependent, it is therefore necessary to artificially determine that a kind of vector puts in order.In the embodiment of the present invention In, the MAC ascending orders arrangement using AP is used as sort method.
203rd, by the AP combination of two after sequence into (APi,APj) (wherein, 0<i<J≤m, m represent all AP number), AP is divided into according to MAC AddressGroup.Go out the two of corresponding A P combinations from each RSSI extracting datas that marked sampled point RSSI vectors are tieed up, as classification initial data.Such as table 2, shown in table 3.
Table 2 extracts data format
School punctuate numbering AP1 AP2
1 -85 -97
1 -83 -92
2 -70 -73
…… …… ……
Table 3 extracts data format
School punctuate numbering AP2 AP3
1 -97 -63
1 -92 -65
2 -73 -95
…… …… ……
204th, using the vector that step 203 constructs as input, using the distance between vector as measuring similarity function, use It can find that the X-means algorithms of clusters number carry out cluster analysis automatically.The each two-dimentional AP combinations of record are to whole positioning respectively The dividing condition in region.
The specific implementation process of X-means algorithm cluster analyses is as follows:
Step1. clusters number k scopes [k is specifiedmin,kmax], and initialize k=kmin.K scope is according to actual area to be measured The size selection in domain, the scope per sub-regions is in 200m2To 700m2, [k is calculated in this approachmin,kmax];
Step2. k AP number of combinations strong point u is randomly selected in the set of eigenvectors EV extracted from step 2021,u2, u3...ukAs initial cluster center;Set of eigenvectors EV such as tables 2, shown in table 3, select k characteristic vector and be used as just Beginning center;
Step3. for each AP number of combinations strong point x in set of eigenvectors EVi, according to belonging to similarity judges it Class cluster,Wherein s (arg1,arg2) it is Similarity Measure function;
Step4. above procedure is repeated, all data points are all assigned to most like class cluster, so as to by all AP groups Data point all Preliminary divisions are into corresponding class cluster;
Step5. for each class cluster, its cluster centre is recalculated,Its In, c(i)Represent data point xiIt is preliminary to assert affiliated type;c(i)=j is referred to:If data point xiBelong to class cluster j, then (c(i)=j)=1, otherwise (c(i)=j)=0;The center represents the weighted average center position of each class cluster;
Step6. calculation criterion function,Wherein xiIt is the data in data set Point, ujIt is class cluster j cluster centre;K refers to the number of cluster centre;
Step7. if criterion function, which no longer changes, turns to Step8, illustrate that the cluster result has been stablized;Otherwise jump to Step3, re-start cluster;
Step8. to having gathered the bayesian information criterion before and after each class cluster carries out further division and computation partition BICpre,BICpost;Bayesian information criterion (Bayesian Information Criterions, BIC) is bayesian theory An important component, the different models in same data set can be evaluated based on posterior probability, are suitable as Selection complexity is relatively low and the reference frame of preferable model is described to data set.
Wherein for Clustering Model corresponding to clusters number k, the calculation formula of bayesian information criterion:Wherein, EV is the set of the characteristic vector extracted in step 202;R is in EV Comprising characteristic vector number, here characteristic vector number be equal to AP groups collect all positions RSSI combine Number;P represents number of parameters, referred to as Schwarz criterions, and its calculation formula is p=k+kd in the present invention, wherein, d EV The dimension of middle characteristic vector, i.e. d=2;It is considered as the punishment to Clustering Model complexity;It is cluster mould Type MkMaximum posteriori log-likelihood estimation on characteristic vector set EV, its calculation formula such as following formula institute
Wherein,u(i)For class cluster i cluster centre;
Step9. if BICpre> BICpost, whether than original score higher, fraction height then receives point if watching results model Split, turn to Step10, otherwise make k=k+1 and jump to Step8;
Step10. if k > kmax, then need to re-start cluster, turn to Step7;Otherwise make k=k+1 and jump to Step2, calculate the cluster situation of one class of increase;
Step11. the maximum dividing modes of BIC are chosen as cluster result;
It is assumed that M is model set corresponding to different clusters number k, then have It is as optimal poly- Class model.Each type is expressed as a kind of signal characteristic, and signal characteristic reflects the aggregation of the signal distributions in a certain region Phenomenon.
Fig. 3 is another cluster flow chart of the inventive method, and the flow belongs to a part for offline sample phase.Specifically It may include steps of:
301st, smart mobile phone high frequency sweep periphery AP signals, the data format scanned such as table 1 are used in each school punctuate It is shown.It should be noted that the number of data of each school punctuate collection is not fixed, it is different because of acquisition time length.It is if current Position fails to collect corresponding AP RSSI, is filled up with -100dB.
302nd, AP is divided into m groups according to MAC Address (m represents all AP number).From marked each of sampled point RSSI extracting datas go out corresponding A P one-dimensional RSSI vectors, as classification initial data.Such as table 4, shown in table 5.
Table 4 extracts data format
School punctuate numbering AP1
1 -85
1 -83
2 -70
…… ……
Table 5 extracts data format
School punctuate numbering AP2
1 -97
1 -92
2 -73
…… ……
303rd, using the vector that step 302 constructs as input, carried out using the distance between vector as measuring similarity function Cluster analysis, cluster analysis is using the X-means algorithms that can find clusters number automatically, and clustering method is similar with step 203, area The one-dimensional vector of single AP be not changed in the bivector for combining the characteristic vector in step 203 by AP.Signal mode is anti- The clustering phenomena of the signal distributions in a certain region has been reflected, has recorded dividing conditions of each AP to whole localization region respectively.Need It should be noted that for same localization region, divisions of the different AP to the region can be different, and its reason is these AP Deployed position spatially wide apart, it is abnormal by non line of sight transmission effects, multipath transmisstion effect and RSSI attenuation laws It is different, it is thus possible to difference to be produced to division result.
Fig. 4 is the training flow chart of the inventive method, and the flow belongs to a part for offline sample phase.It can specifically wrap Include following steps:
401st, the two-dimentional RSSI vectors of AP combination of two are extracted in each calibration point, such as table 2, shown in table 3, and will be demarcated The numbering of point replaces with the class number after corresponding cluster.
402nd, single AP one-dimensional RSSI vectors are extracted in each calibration point, such as table 4, shown in table 5, and by calibration point Numbering replaces with the class number after corresponding cluster.
403rd, to step 401,402 obtained vectors carry out SVM training respectively, calculate the characteristic of division of SVMs Value.The follow-up validity for judging initialisation range and reducing orientation range that is calculated as of characteristic of division value provides data support. The characteristic of division that the present embodiment is chosen is exactly the RSSI vectors that step 401,402 obtains.
Fig. 5 is a kind of coarseness positioning flow figure of the inventive method, and the flow belongs to a part for on-line stage.Specifically It may include steps of:
501st, each two-dimentional AP groups (AP trained is loadedi,APj) svm classifier model, read and currently collect RSSI, according to AP MAC Address ascending sort, then extract every group of AP combination and be used as class vector.It should be noted that SVM is calculated Method is order dependent with vector, it is therefore necessary to artificially determines that a kind of vector puts in order.In this example, using AP MAC ascending orders Arrangement is used as sort method.
502nd, the AP for extracting step 501 combines the class vector to be formed, and it is carried out using corresponding SVM models pre- Survey, obtain every group of AP respectively under its corresponding region partition mode in the probability of regional.Because current location is likely to be at The edge in multiple regions, or because some in two-dimentional AP combination or some AP are due to by non line of sight transmission effects, more The reasons such as footpath propagation effect and RSSI attenuation laws are abnormal cause RSSI to fluctuate, it is possible that multiple regions are all satisfactory Situation, it can be chosen in accordance with the following methods:
The characteristic vector that each AP samplings extract, multiple prediction knots are might have after corresponding SVM model predictions Fruit meets the requirements, and each prediction result corresponds to the sub-regions that the AP groups mark off area to be targeted.Behalf in above formula Qualified prediction result number, that is, represent SVM qualification of model current devices and be likely to be in several sub-regions;areakTable Show k-th of qualified region, that is, representing SVM qualification of model current device may be in which sub-regions.Area(APi, APj) represent AP groups (APi,APj) determined by region where current location, that is, represent SVM models and think that current device may Union in these subregions.The choosing method of satisfactory subregion is existed if predicting current signature vector The probability of certain sub-regions is not less than some threshold epsilon (0<ε<1) subregion, is considered as to meet the requirements.In this example, selection ε=1/n, n represent the subregion number that AP combinations mark off.
503rd, all AP groups (AP to being obtained in step 502i,APj) Area (APi,APj) use " ballot " mode meter Calculate positioning result.May be in a certain region if the sample data of certain AP combinations is identified as by step 502 prediction, should Region poll adds 1.Travel through the Area (AP of all AP combinationsi,APj) and vote, the most region of poll is selected as the thick of positioning Granularity localization region, the poll in each region arrive 0Between.From geometrically showing as choosing by Area (APi,APj) covering Localization region of the most region of number as coarseness.If the poll of all areas is both less than a certain threshold xi, it is believed that positioning Failure, terminate positioning;If there is multiple regions poll at most and be more than ξ, then the union in these regions is sought, as positioning Coarseness localization region.Because normal positioning at least needs 3 AP to participate in calculating, therefore in this example, ξ values are 4.
Fig. 6 is a kind of fine granularity positioning flow figure of the inventive method, and the flow belongs to a part for on-line stage.Specifically It may include steps of:
601st, the svm classifier model for each AP that loading trains before, the RSSI currently collected is read, by the RSSI An one-dimensional class vector is formed, each AP trained before use svm classifier model is predicted to it, asked respectively Go out each AP under its corresponding region partition mode in the probability of regional, therefrom choose the coarseness positioning that previous step is obtained The probability of regional in the R of region.Because current location is likely to be at the edge in multiple regions, or due to the AP due to by RSSI is caused to fluctuate to reasons such as non line of sight transmission effects, multipath transmisstion effect and RSSI attenuation laws exceptions, it is possible that Multiple regions are all more conform with the situation of requirement, can be chosen in accordance with the following methods:
The characteristic vector that each AP samplings extract, multiple prediction knots are might have after corresponding SVM model predictions Fruit meets the requirements, and each prediction result corresponds to the sub-regions that the AP marks off area to be targeted.Behalf accords with above formula The prediction result number of conjunction condition, that is, represent SVM qualification of model current devices and be likely to be in several sub-regions;areakRepresent K-th of qualified region, that is, representing SVM qualification of model current device may be in which sub-regions, and the region is necessary It is the coarseness localization region R obtained in step 4 subset.Area(APi) represent APiArea where identified current location Domain, that is, represent SVM models and think that current device is likely to be at the union of these subregions.The selection of satisfactory subregion Method is, if the probability for predicting current signature vector in certain sub-regions is not less than some threshold epsilon (0<ε<1), it is considered as The subregion meets the requirements.In this example, ε=1/n of selection, n represent the subregion number that the AP is marked off.
602nd, to all AP in step 601iArea (APi) calculated using " ballot " mode, i.e., if certain AP sample Data are identified as in a certain region by step 601 prediction, then the region poll adds 1.Travel through all AP Area (APi) And vote, the most region of poll is selected as the fine granularity localization region positioned, and the poll in each region is 0 between m.From Geometrically show as choosing by Area (APi) the most region of degree of covering as coarseness localization region.If all areas The poll in domain is both less than a certain threshold xi, it is believed that positioning failure, terminates positioning;It is at most and big if there is the poll in multiple regions In ξ, then the union in these regions is sought, obtains its center point coordinate and radius, as final localization region.Due to normal positioning 3 AP are at least needed to participate in calculating, therefore in this example, ξ values are 4.
Non-elaborated part of the present invention belongs to techniques well known.
It is described above, part embodiment only of the present invention, but protection scope of the present invention is not limited thereto, and is appointed What those skilled in the art the invention discloses technical scope in, the change or replacement that can readily occur in should all be covered Within protection scope of the present invention.

Claims (4)

1. a kind of indoor orientation method based on WLAN, it is characterised in that realize that step is as follows:
Step 1:The RSSI data predictions for each AP that sampled point is collected, therefrom extract a peacekeeping bivector point Zuo Wei not characteristic vector;
Step 2:Feature vector clusters are analyzed, area to be targeted is divided into multiple positioning subregions;
Step 3:For every group of characteristic vector combination cluster result, respective disaggregated model is respectively trained out;Based on classification Models coupling " ballot " mechanism chooses poll highest subregion set from all subregions;
Step 4:Positioned using two-wheeled and reduce subregion range of convergence, improve positioning precision;
The step 1 extracts a peacekeeping bivector respectively as characteristic vector from preprocessed data, including:
(1) by all AP scanned according to MAC Address ascending sort;
(2) a peacekeeping bivector is extracted as characteristic vector according to following two methods:
A. by the AP combination of two after sequence, AP is divided into according to MAC AddressGroup, every group of AP are expressed as (APi,APj), its In, 0<i<J≤m, m represent all AP number, and the vector that these AP combination compositions are gone out from pretreated extracting data is made It is characterized vector;
B. each AP is separately as one group, will all offline gathered datas be divided into m groups, every group of AP table according to AP MAC Address It is shown as APi, wherein, 0<I≤m, m represent all AP number, from pretreated extracting data go out these AP composition to Amount is used as characteristic vector;
The step 3, specific implementation process include off-line phase and on-line stage;
Off-line phase, the characteristic vector constructed for the two kinds of structures method proposed in step 1, is respectively trained out every kind of structure Make SVMs (Support Vector Machine, SVM) disaggregated model corresponding to every feature vectors of method;
On-line stage, from real time data extraction characteristic of division vector, the svm classifier model that off-line phase trains is read, according to Supporting vector polynomial expansion entry value, the probability that vector to be sorted corresponds to different zones is calculated, with reference to " ballot " mechanism from institute Have and poll highest set of regions R is chosen in region;
The voting mechanism refers to, if AP groups (APi,APj) sample data by SVM prediction be identified as in a certain region Interior, then the region poll adds 1;Travel through the EV (AP of all AP groupsi,APj) and vote, the most region of poll is selected as positioning Coarseness localization region, the poll in each region should arrive 0Between, EV is characterized vector set;
The step 4, positioned using two-wheeled and reduce set of regions scope, be implemented as:
(1) the svm classifier model trained is read, calculates supporting vector polynomial expansion entry value;
(2) RSSI currently collected, extraction characteristic of division vector are read, and characteristic of division is standardized;
(3) by Polynomial kernel function by characteristic of division DUAL PROBLEMS OF VECTOR MAPPING to higher dimensional space, and according to the supporting vector multinomial Deploy entry value and calculate the probability that vector to be sorted corresponds to different zones, the coarseness positioning area therefrom obtained in selecting step three The probability of regional in the R of domain;
(4) for each APi, judge whether every sub-regions for marking off are eligible, and the subregion is obtained in step 3 Coarseness localization region R subset, it is eligible if there is more sub-regions, then SVM models think current device may In union in these subregions;
(5) poll highest set of regions R ' is chosen from R with reference to " ballot " mechanism, specific steps include:If APiSample number It is identified as according to by SVM predictions in a certain region, then the region poll adds 1, is voted according to each AP localization region, choosing Fine granularity localization region of the most region of booking number as positioning, the poll in each region should be 0 between m.
2. the indoor orientation method according to claim 1 based on WLAN, it is characterised in that:The step 1 is by sampled point The each AP collected RSSI data predictions, including:The too low data of RSSI are deleted, non-locating AP data is deleted, fills out Mend the RSSI data not being scanned;
Deletion data too low RSSI refer to, data of the RSSI intensity less than a certain threshold value are deleted;The deletion is non-fixed Position AP data refer to, delete the RSSI for the AP for being unsuitable for positioning, and the feature for being unsuitable for positioning AP is that intensity is too low, i.e., RSSI is small In -95dB or less stable, i.e. variance is more than 20.
3. the indoor orientation method according to claim 1 based on WLAN, it is characterised in that:In the step 2, to spy Vector clusters analysis is levied, area to be targeted is divided into multiple positioning subregions, concretely comprised the following steps:With the spy constructed in step 1 Sign vector is input, and cluster analysis is carried out using the distance between characteristic vector as measuring similarity function, and cluster analysis uses The X-means algorithms of clusters number can be found automatically.
4. the indoor orientation method according to claim 1 based on WLAN, it is characterised in that:The tuning on-line stage Concrete operations include:
(1) the svm classifier model trained is read, calculates supporting vector polynomial expansion entry value;
(2) RSSI currently collected, extraction characteristic of division vector are read;
(3) by Polynomial kernel function by characteristic of division DUAL PROBLEMS OF VECTOR MAPPING to higher dimensional space, and according to the supporting vector multinomial Deploy entry value and calculate the probability that vector to be sorted corresponds to different zones;
(4) for each AP groups (APi,APj), judge whether every sub-regions for marking off are eligible, if there is more height Region is eligible, then SVM models think that current device is likely to be in the union of these subregions.
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Families Citing this family (17)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104463929B (en) * 2014-12-16 2017-07-18 重庆邮电大学 Indoor WLAN signal mapping and mapping method based on Image Edge-Detection signal correlation
CN104853434A (en) * 2015-01-13 2015-08-19 中山大学 Indoor positioning method based on SVM and K mean value clustering algorithm
CN105044662B (en) * 2015-05-27 2019-03-01 南京邮电大学 A kind of fingerprint cluster multi-point joint indoor orientation method based on WIFI signal intensity
CN105334493B (en) * 2015-10-09 2017-12-26 北京航空航天大学 A kind of indoor orientation method based on WLAN
CN109212464B (en) * 2016-06-06 2020-01-14 中科劲点(北京)科技有限公司 Method and equipment for estimating terminal distance and position planning
CN106131958A (en) * 2016-08-09 2016-11-16 电子科技大学 A kind of based on channel condition information with the indoor Passive Location of support vector machine
CN106131959B (en) * 2016-08-11 2019-05-14 电子科技大学 A kind of dual-positioning method divided based on Wi-Fi signal space
CN106412838B (en) * 2016-09-10 2019-10-18 华南理工大学 A kind of bluetooth indoor orientation method based on statistical match
CN106643736B (en) * 2017-01-06 2020-05-22 中国人民解放军信息工程大学 Indoor positioning method and system
CN107087256A (en) * 2017-03-17 2017-08-22 上海斐讯数据通信技术有限公司 A kind of fingerprint cluster method and device based on WiFi indoor positionings
CN107290714B (en) * 2017-07-04 2020-02-21 长安大学 Positioning method based on multi-identification fingerprint positioning
CN108712723B (en) * 2018-05-08 2019-05-31 深圳市名通科技股份有限公司 AP similarity determines method, terminal and computer readable storage medium
CN108924756B (en) * 2018-06-30 2020-08-18 天津大学 Indoor positioning method based on WiFi dual-band
CN109286900B (en) * 2018-08-29 2020-07-17 桂林电子科技大学 Wi-Fi sample data optimization method
CN109640262B (en) * 2018-11-30 2021-01-05 哈尔滨工业大学(深圳) Positioning method, system, equipment and storage medium based on mixed fingerprints
CN110213710A (en) * 2019-04-19 2019-09-06 西安电子科技大学 A kind of high-performance indoor orientation method, indoor locating system based on random forest
CN110519692B (en) * 2019-09-12 2020-10-02 中南大学 Positioning and partitioning method based on Bayes-k mean clustering

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103402256A (en) * 2013-07-11 2013-11-20 武汉大学 Indoor positioning method based on WiFi (Wireless Fidelity) fingerprints
CN103533647A (en) * 2013-10-24 2014-01-22 福建师范大学 Radio frequency map self-adaption positioning method based on clustering mechanism and robust regression
CN103648106A (en) * 2013-12-31 2014-03-19 哈尔滨工业大学 WiFi indoor positioning method of semi-supervised manifold learning based on category matching

Family Cites Families (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CA2638754C (en) * 2001-09-05 2013-04-02 Newbury Networks, Inc. Position detection and location tracking in a wireless network
US8224349B2 (en) * 2010-02-25 2012-07-17 At&T Mobility Ii Llc Timed fingerprint locating in wireless networks

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103402256A (en) * 2013-07-11 2013-11-20 武汉大学 Indoor positioning method based on WiFi (Wireless Fidelity) fingerprints
CN103533647A (en) * 2013-10-24 2014-01-22 福建师范大学 Radio frequency map self-adaption positioning method based on clustering mechanism and robust regression
CN103648106A (en) * 2013-12-31 2014-03-19 哈尔滨工业大学 WiFi indoor positioning method of semi-supervised manifold learning based on category matching

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
A Novel Algorithm for Enhancing Accuracy of Indoor Position Estimation;Xiaoqing Lu et al.;《Proceeding of the 11th World Congress on Intelligent Control and Automation》;20140704;全文 *
A Novel Clustering-Based Approach of Indoor Location Fingerprinting;Chung-Wei Lee et al.;《2013 IEEE 24th International Symposium on Personal Indoor and Mobile Radio Communications (PIMRC)》;20131125;全文 *

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