CN107087276A - A kind of fingerprint base method for building up and device based on WiFi indoor positionings - Google Patents
A kind of fingerprint base method for building up and device based on WiFi indoor positionings Download PDFInfo
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- CN107087276A CN107087276A CN201710160759.XA CN201710160759A CN107087276A CN 107087276 A CN107087276 A CN 107087276A CN 201710160759 A CN201710160759 A CN 201710160759A CN 107087276 A CN107087276 A CN 107087276A
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
- H04W16/22—Traffic simulation tools or models
- H04W16/225—Traffic simulation tools or models for indoor or short range network
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W64/00—Locating users or terminals or network equipment for network management purposes, e.g. mobility management
- H04W64/006—Locating users or terminals or network equipment for network management purposes, e.g. mobility management with additional information processing, e.g. for direction or speed determination
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Abstract
The invention provides a kind of fingerprint base method for building up and its device based on WiFi indoor positionings, wherein, the fingerprint base method for building up includes：S1 selected one does not set up the WAP of fingerprint base；S2 is in target area for the selected multiple sampled points of WAP stochastical sampling；S3 sets up the signal intensity profile model of each point according to the Gaussian process of sampled point, completes the foundation of WAP fingerprint base in the target area；S4 judges whether that indoor all WAPs all establish corresponding fingerprint base, if so, completing the foundation of target area fingerprint base；Otherwise step S1 is jumped to.Its WiFi fingerprint for obtaining continuous whole plane according to discrete WiFi fingerprints distribution is distributed, and especially for the region being not directed in random sampling procedure, can equally be covered by this method, and the precision of indoor WiFi positioning is greatly promoted with this.
Description
Technical field
The present invention relates to field of locating technology, more particularly to a kind of fingerprint base method for building up and device.
Background technology
With the fast development of development of Mobile Internet technology, location Based service demand therewith more and more extensively, permeate in
The various aspects such as military, business, life.At present, for positioning, people it is most-often used be global positioning system (Global
Positioning System, GPS), it gives user by the signal time difference between multi-satellite and the GPS of user
Positioning, orientation range can be covering the whole world, but satellite-signal is easily blocked by various barriers, i.e., location technology is not particularly suited for
Indoor or built-up occasion, need to carry out indoor positioning using other technological means.
Indoor positioning technologies based on WiFi (Wireless Fidelity, Wireless Fidelity) fingerprint base are obtained in recent years
Extensive concern, the technology mainly by multiple by dividing in advance and collecting multiple nothings in regions with positional information
Received signal strength (Received Signal Strength, RSS) value of line Network Access Point (Access Point, AP),
Set up the indoor positioning fingerprint base of a RSS value corresponding relation being collected into comprising each location point and the location point, with this,
When being positioned to mobile device, it can be matched by finding the RSS values being collected into current mobile device in fingerprint base
Location point, realize positioning.
At present for the research of the indoor locating system based on WiFi, the foundation and implementation of WiFi fingerprint bases are concentrated mainly on
Position two aspects.Wherein, WiFi fingerprint bases are the set of all WiFi fingerprints collected, although fingerprint base is all based on adopting
The fingerprint that sample is obtained, but different processing modes can obtain different types of fingerprint base, the locating effect of realization is different, and it is determined
Locating effect of the WiFi fingerprint location systems in implementation process is determined, so WiFi fingerprint bases is accurate with being reliably very heavy
Want.At this stage, WiFi fingerprints are in general obtained by the non-supervisory method of sampling to map, but its sampled value is mainly divided
It is distributed in start position and sample path, reliable finger print data can not be then provided for other regions.Especially sample just
During the phase, the fingerprint quantity of collection is relatively fewer, it is impossible to which meeting the demand of user, there is provided satisfied positioning precision.
The content of the invention
In view of the above-mentioned problems, the invention provides a kind of fingerprint base method for building up and device based on WiFi indoor positionings,
Effectively solve in the prior art due to the technical problem of the very few influence positioning precision of WiFi fingerprint numbers of samples.
The technical scheme that the present invention is provided is as follows：
A kind of fingerprint base method for building up based on WiFi indoor positionings, interior includes multiple WAPs, the fingerprint
Storehouse method for building up includes：
S1 selected one does not set up the WAP of fingerprint base；
S2 is in target area for the selected multiple sampled points of WAP stochastical sampling；
S3 sets up the RSS distributed models of each point according to the Gaussian process of sampled point, completes the WAP in target area
The foundation of fingerprint base in domain；
S4 judges whether that indoor all WAPs all establish corresponding fingerprint base, if so, completing target area
The foundation of fingerprint base；Otherwise step S1 is jumped to.
It is further preferred that specifically including in step s3：
S31 obtains the degree of correlation between each point signal strength values according to the degree of correlation between sampled point；
S32 obtains RSS distributed models according to the degree of correlation between each point signal strength values；
S33 obtains the corresponding signal strength values in any point in target area according to RSS distributed models, completes this and wirelessly connects
The foundation of access point fingerprint base in the target area.
It is further preferred that in step S31：
Degree of correlation k (x between sampled pointi,yj) be：
Degree of correlation cov (x between each point signal strength valuesi,yj) be：
Wherein,Represent the sample variance of sampled point；L represents signal strength space correlation yardstick variable；Represent
Gaussian noise；As i=j, δ (xi,yj) value be 1, be otherwise 0.
It is further preferred that in step s 32, it is right according to the degree of correlation between the signal strength values obtained in step S31
Each point signal intensity matrix Y degree of correlation cov (Y) is obtained in sampling dot matrix X：
Wherein, K represents covariance matrix K [i, j]=k (x according to the sampling dot matrix X n × b tried to achievei,yj)；Obtain
RSS distributed models：
It is further preferred that in step S33, any point x in target area*Signal strength values y*For：
Wherein,k*For point x*
With sample dot matrix X covariance matrix, wherein, value k (i)=k (x in the covariance matrix*,xi)。
Device is set up present invention also offers a kind of fingerprint base based on WiFi indoor positionings, interior wirelessly connects including multiple
Access point, the fingerprint base, which sets up device, to be included：
Information receiving module, for receiving multiple sampled points for selected wireless access point sampling；
Model building module, the Gaussian process of the sampled point for being received according to information receiving module sets up each point
RSS distributed models, complete the foundation of WAP fingerprint base in the target area；
Judge module, for judging whether that indoor all WAPs all establish corresponding fingerprint base.
Also include it is further preferred that the fingerprint base is set up in device：
Computing module, each point signal intensity is obtained for the degree of correlation between the sampled point that is received according to information receiving module
The degree of correlation between value, and calculated for the RSS distributed models set up according to model building module and obtain appointing in target area
Any corresponding signal strength values of meaning；
Model building module obtains RSS distributed models according to the degree of correlation between each point signal strength values.
It is further preferred that in computing module：
Degree of correlation k (x between sampled pointi,yj) be：
Degree of correlation cov (x between each point signal strength valuesi,yj) be：
Wherein,Represent the sample variance of sampled point；L represents signal strength space correlation yardstick variable；Represent high
This noise；As i=j, δ (xi,yj) value be 1, be otherwise 0.
It is further preferred that in model building module：
The degree of correlation between obtained signal strength values is calculated according to computing module, each point is obtained for sampling dot matrix X
Signal intensity matrix Y degree of correlation cov (Y)：
Wherein, K represents covariance matrix K [i, j]=k (x according to the sampling dot matrix X n × b tried to achievei,yj)；
And then obtain RSS distributed models：
It is further preferred that in computing module：
The RSS distributed models set up according to model building module, any point in target area is calculated according to below equation
x*Signal strength values y*：
Wherein,k*For point x*
With sample dot matrix X covariance matrix, wherein, value k (i)=k (x in the covariance matrix*,xi)。
In the present invention, stochastical sampling is carried out to each indoor WAP, obtains the WiFi fingerprints of limited quantity, and
The RSS distributed models of each point are set up according to the Gaussian process of sampled point, the signal strength values of each point in target area are calculated, it is complete
Into the foundation of WAP fingerprint base in the target area, and circulated with this, RSS is set up to indoor each WAP
Distributed model.
Its WiFi fingerprint for obtaining continuous whole plane according to discrete WiFi fingerprints distribution is distributed, especially for random
The region being not directed in sampling process, can equally be covered by this method；In addition, for WiFi fingerprint collectings initial stages,
In the situation of fingerprint collecting negligible amounts, RSS distributed models can be also set up according to this method, any position in target area is obtained
Corresponding signal strength values (fingerprint value) are put, WiFi fingerprints are expanded to the whole plane of target area by one-dimensional sample path
Covering is helped to position there is provided more WiFi fingerprints quantity, and the precision of indoor WiFi positioning is greatly promoted with this.
Brief description of the drawings
Below by clearly understandable mode, preferred embodiment is described with reference to the drawings, to above-mentioned characteristic, technical characteristic,
Advantage and its implementation are further described.
Fig. 1 is a kind of embodiment schematic flow sheet of fingerprint base method for building up based on WiFi indoor positionings in the present invention；
Fig. 2 is the fingerprint base method for building up another embodiment flow signal based on WiFi indoor positionings in the present invention
Figure；
Fig. 3 is the sampling point distributions in an example of the invention for a certain WAP；
The RSS distributed models that Fig. 4 sets up for the present invention according to sampled point as shown in Figure 3；
Fig. 5 sets up a kind of embodiment schematic diagram of device for the fingerprint base based on WiFi indoor positionings in the present invention；
Fig. 6 sets up device another embodiment schematic diagram for the fingerprint base based on WiFi indoor positionings in the present invention.
Reference：
100- fingerprint bases set up device, 110- information receiving modules, 120- model building modules, 130- judge modules,
140- computing modules.
Embodiment
In order to illustrate more clearly about the embodiment of the present invention or technical scheme of the prior art, control is illustrated below
The embodiment of the present invention.It should be evident 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, and obtain other embodiments.
Barogram is widely used in meteorological research, and it can be obtained by arbitrary target position by setting up barogram
Atmospheric pressure value, it is known that correspondingly atmospheric pressure value is also higher in the high place of latitude, apart from the nearlyer RSS of the distance of WAP
Value is also bigger；Temperature and humidity change, and the factor such as air motion can cause the multipath under the fluctuation of air pressure, indoor scene to imitate
Should, the factor such as non-line-of-sight propagation also result in the signal fluctuation phenomenon for not deferring to propagation model.Understand, similar to atmospheric pressure value
Consecutive variations, RSS values are also consecutive variations indoors, i.e., when the RSS values of target area position are undergone mutation, its neighbouring position
The change of same trend can be also presented in the RSS values put.
With this, if the problems such as the WiFi Fingerprint separations existed for current WiFi indoor positionings, negligible amounts, in target area
The RSS distributed models of similar " barogram " can be obtained in domain, then can just obtain the approximate RSS of target area optional position
Vector, is solved in the prior art due to the technical problem of the very few influence positioning precision of WiFi fingerprint numbers of samples.
In addition, it is known that for the set of a stochastic variable composition, if arbitrary finite stochastic variable in set
All obey Joint Gaussian distribution, then this process is referred to as Gaussian process.It is prevalent in every field, such as engineering
Habit, information network, data visualization and computer animation etc..
Based on this, the invention provides a kind of fingerprint base method for building up based on WiFi indoor positionings, it is assumed that interior includes
Multiple WAPs, gather WiFi fingerprints, and be directed to for each WAP one by one in the fingerprint base method for building up
The WiFi fingerprints collected obtain RSS distributed models according to Gaussian process, complete the foundation of fingerprint base.
It is as shown in Figure 1 a kind of embodiment schematic flow sheet of the fingerprint base method for building up, it can be seen that at this
Fingerprint base method for building up includes：S1 selected one does not set up the WAP of fingerprint base；S2 is directed to selected in target area
The multiple sampled points of WAP stochastical sampling；S3 sets up the RSS distributed models of each point according to the Gaussian process of sampled point, completes
The foundation of WAP fingerprint base in the target area；S4 judges whether that indoor all WAPs are all established accordingly
Fingerprint base, if so, completing the foundation of target area fingerprint base；Otherwise step S1 is jumped to.
Above-mentioned embodiment is improved and obtains present embodiment, as shown in Fig. 2 in the present embodiment, the fingerprint
Storehouse method for building up includes：S1 selected one does not set up the WAP of fingerprint base；S2 is wireless for what is selected in target area
The multiple sampled points of access point stochastical sampling；S31 obtains the phase between each point signal strength values according to the degree of correlation between sampled point
Guan Du；S32 obtains RSS distributed models according to the degree of correlation between each point signal strength values；S33 is obtained according to RSS distributed models
The corresponding signal strength values in any point in target area, complete the foundation of WAP fingerprint base in the target area；
S4 judges whether that indoor all WAPs all establish corresponding fingerprint base, if so, completing target area fingerprint base
Set up；Otherwise step S1 is jumped to.
Assuming that Gaussian process has one group of training sample for including noise：
D={ (x1,y1),(x2,y2),...,(xn,yn)}
Then：
yi=f (xi)+ε
Wherein, xiRepresent d dimension spacesIn independent variable sample value, yiDesired value/observed value is represented, noise ε represents equal
Value is that 0, variance isGaussian Profile.
Based on this, in the above-described embodiment, n input is represented from scalar x with n × d matrix X, n observed value
Represented with the matrix Y of n × 1.Specifically, in RSS distributed models, X represents n sampling point position coordinate of target area, and Y is represented
Selected WAP is in the corresponding signal strength values of these sampled points (RSS values).
Gaussian process estimates Posterior distrbutionp by function f and training sample D, its core be between different observed values be
Related, i.e. f (xi) and f (yi) covariance depend on xiWith yiCovariance.In view of Gaussian noise, between sampled point
Covariance function k (xi,yj) be specially：
Degree of correlation cov (x between each point signal strength valuesi,yj) be：
Wherein,Represent the sample variance of sampled point；L represents signal strength space correlation yardstick variable, to determine
Signal intensity correlation is smaller between the speed that correlation rises or falls between 2 points, the bigger expression sampled equidistant points of l；Represent Gaussian noise；As i=j, δ (xi,yj) value be 1, be otherwise 0.
With this, for input position coordinate sample matrix X (sampling dot matrix), each position coordinate points signal intensity square is obtained
Battle array Y degree of correlation cov (Y) be：
Wherein, K represents covariance matrix K [i, j]=k (x according to the sampling dot matrix X n × b tried to achievei,yj).It can send out
Existing signal strength values are Gaussian Profile：RSS distributed models are obtained with this.
As can be seen that whenever there is one group of sampling dot matrix X input from above formula, it is possible to utilize covariance matrix K descriptions
Sampled point is as the degree of correlation between coordinate and signal strength values, with this, for selected WAP, appoints in target area
Anticipate a point x*Signal strength values y*For：
Wherein,k*For point x*
With sample dot matrix X covariance matrix, wherein, value k (i)=k (x in the covariance matrix*,xi)。
In an example, the original sample point of a certain wireless aps in target area is illustrated in figure 3, based on foregoing description
The RSS distributed models set up of fingerprint base method for building up as shown in figure 4, specific, in figs. 3 and 4, transverse and longitudinal coordinate x/y is represented
The coordinate position (two-dimensional coordinate) of sampled point, z-axis represents the corresponding signal strength values of sampled point.
The fingerprint base based on WiFi indoor positionings of the invention provided is provided and sets up a kind of embodiment party of device 100
Formula schematic diagram, applied to above-mentioned fingerprint method for building up, it is assumed that interior includes multiple WAPs, in the fingerprint base method for building up
In gather WiFi fingerprints for each WAP one by one, and obtained for the WiFi fingerprints that collect according to Gaussian process
RSS distributed models, complete the foundation of fingerprint base.Specifically, include in the fingerprint base device 100：Information receiving module 110, mould
Type sets up module 120 and judge module 130, wherein, model building module 120 respectively with information receiving module 110 and judge
Module 130 is connected.
In the course of the work, have selected one do not set up the WAP of fingerprint base after, in target area for selected
The multiple sampled points of WAP stochastical sampling and send it to fingerprint base and set up device 100.Fingerprint is set up in device 100
Information receiving module 110 receive multiple sampled points for selected wireless access point sampling after, model building module 120
The Gaussian process of the sampled point received according to information receiving module 110 sets up the RSS distributed models of each point, completes this and wirelessly connects
The foundation of access point fingerprint base in the target area；Finally, judge module 130 judges whether that indoor all WAPs are all set up
Corresponding fingerprint base, if so, complete the foundation of target area fingerprint base.
Above-mentioned embodiment is improved and obtains present embodiment, as shown in fig. 6, in the present embodiment, fingerprint base
Set up in device 100 in addition to including above- mentioned information receiving module 110, model building module 120 and judge module 130,
Also include the computing module 140 being connected respectively with information receiving module 110 and model building module 120.
In the course of the work, have selected one do not set up the WAP of fingerprint base after, in target area for selected
The multiple sampled points of WAP stochastical sampling and send it to fingerprint base and set up device 100.Fingerprint is set up in device 100
Information receiving module 110 receive multiple sampled points for selected wireless access point sampling after, the basis of computing module 140
The degree of correlation between the sampled point that information receiving module 110 is received obtains the degree of correlation between each point signal strength values, with this mould
Type sets up module 120 and obtains RSS distributed models according to the degree of correlation between each point signal strength values, and computing module 140 is further
The RSS distributed models set up according to model building module 120 calculate and obtain the corresponding signal intensity in any point in target area
Value, completes the foundation of WAP fingerprint base in the target area；Finally, judge module 130 judges whether indoor all
WAP all establishes corresponding fingerprint base, if so, completing the foundation of target area fingerprint base.
Assuming that Gaussian process has one group of training sample for including noise：
D={ (x1,y1),(x2,y2),...,(xn,yn)}
Then：
yi=f (xi)+ε
Wherein, xiRepresent d dimension spacesIn independent variable sample value, yiDesired value/observed value is represented, noise ε represents equal
Value is that 0, variance isGaussian Profile.
Based on this, in the above-described embodiment, n input is represented from scalar x with n × d matrix X, n observed value
Represented with the matrix Y of n × 1.Specifically, in RSS distributed models, X represents n sampling point position coordinate of target area, and Y is represented
Selected WAP is in the corresponding signal strength values of these sampled points (RSS values).
Gaussian process estimates Posterior distrbutionp by function f and training sample D, its core be between different observed values be
Related, i.e. f (xi) and f (yi) covariance depend on xiWith yiCovariance.In view of Gaussian noise, between sampled point
Covariance function k (xi,yj) be specially：
Degree of correlation cov (x between each point signal strength valuesi,yj) be：
Wherein,Represent the sample variance of sampled point；L represents signal strength space correlation yardstick variable, to determine
Signal intensity correlation is smaller between the speed that correlation rises or falls between 2 points, the bigger expression sampled equidistant points of l；Represent Gaussian noise；As i=j, δ (xi,yj) value be 1, be otherwise 0.
With this, for input position coordinate sample matrix X (sampling dot matrix), each position coordinate points signal intensity square is obtained
Battle array Y degree of correlation cov (Y) be：
Wherein, K represents covariance matrix K [i, j]=k (x according to the sampling dot matrix X n × b tried to achievei,yj).It can send out
Existing signal strength values are Gaussian Profile：RSS distributed models are obtained with this.
As can be seen that whenever there is one group of sampling dot matrix X input from above formula, it is possible to utilize covariance matrix K descriptions
Sampled point is as the degree of correlation between coordinate and signal strength values, with this, for selected WAP, appoints in target area
Anticipate a point x*Signal strength values y*For：
Wherein,k*For point x*
With sample dot matrix X covariance matrix, wherein, value k (i)=k (x in the covariance matrix*,xi)。
It should be noted that above-described embodiment can independent assortment as needed.Described above is only the preferred of the present invention
Embodiment, it is noted that for those skilled in the art, is not departing from the premise of the principle of the invention
Under, some improvements and modifications can also be made, these improvements and modifications also should be regarded as protection scope of the present invention.
Claims (10)
1. a kind of fingerprint base method for building up based on WiFi indoor positionings, it is characterised in that interior includes multiple WAPs,
The fingerprint base method for building up includes：
S1 selected one does not set up the WAP of fingerprint base；
S2 is in target area for the selected multiple sampled points of WAP stochastical sampling；
S3 sets up the signal intensity profile model of each point according to the Gaussian process of sampled point, completes the WAP in target area
The foundation of fingerprint base in domain；
S4 judges whether that indoor all WAPs all establish corresponding fingerprint base, if so, completing target area fingerprint
The foundation in storehouse；Otherwise step S1 is jumped to.
2. fingerprint base method for building up as claimed in claim 1, it is characterised in that specifically include in step s3：
S31 obtains the degree of correlation between each point signal strength values according to the degree of correlation between sampled point；
S32 obtains signal intensity profile model according to the degree of correlation between each point signal strength values；
S33 obtains the corresponding signal strength values in any point in target area according to signal intensity profile model, completes this wireless
The foundation of access point fingerprint base in the target area.
3. fingerprint base method for building up as claimed in claim 2, it is characterised in that in step S31：
Degree of correlation k (x between sampled pointi,yj) be：
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Wherein,Represent the sample variance of sampled point；L represents signal strength space correlation yardstick variable；Represent that Gauss makes an uproar
Sound；As i=j, δ (xi,yj) value be 1, be otherwise 0.
4. fingerprint base method for building up as claimed in claim 3, it is characterised in that in step s 32, is obtained according in step S31
The degree of correlation between the signal strength values arrived, each point signal intensity matrix Y degree of correlation cov is obtained for sampling dot matrix X
(Y)：
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Obtain signal intensity profile model：
5. fingerprint base method for building up as claimed in claim 4, it is characterised in that any one in target area in step S33
Point x*Signal strength values y*For：
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Wherein,k*For point x*With adopting
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6. a kind of fingerprint base based on WiFi indoor positionings sets up device, it is characterised in that interior includes multiple WAPs,
The fingerprint base, which sets up device, to be included：
Information receiving module, for receiving multiple sampled points for selected wireless access point sampling；
Model building module, the signal that the Gaussian process of the sampled point for being received according to information receiving module sets up each point is strong
Distributed model is spent, the foundation of WAP fingerprint base in the target area is completed；
Judge module, for judging whether that indoor all WAPs all establish corresponding fingerprint base.
7. fingerprint base as claimed in claim 6 sets up device, it is characterised in that the fingerprint base, which is set up in device, also to be included：
Computing module, for the degree of correlation between the sampled point that is received according to information receiving module obtain each point signal strength values it
Between the degree of correlation, and calculated for the signal intensity profile model set up according to model building module and obtain appointing in target area
Any corresponding signal strength values of meaning；
Model building module obtains signal intensity profile model according to the degree of correlation between each point signal strength values.
8. fingerprint base as claimed in claim 7 sets up device, it is characterised in that in computing module：
Degree of correlation k (x between sampled pointi,yj) be：
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<mo>)</mo>
</mrow>
<mo>=</mo>
<msup>
<msub>
<mi>&sigma;</mi>
<mi>f</mi>
</msub>
<mn>2</mn>
</msup>
<mi>exp</mi>
<mo>&lsqb;</mo>
<mfrac>
<mrow>
<mo>-</mo>
<msup>
<mrow>
<mo>(</mo>
<msub>
<mi>x</mi>
<mi>i</mi>
</msub>
<mo>-</mo>
<msub>
<mi>x</mi>
<mi>j</mi>
</msub>
<mo>)</mo>
</mrow>
<mn>2</mn>
</msup>
</mrow>
<mrow>
<mn>2</mn>
<msup>
<mi>l</mi>
<mn>2</mn>
</msup>
</mrow>
</mfrac>
<mo>&rsqb;</mo>
</mrow>
Degree of correlation cov (x between each point signal strength valuesi,yj) be：
<mrow>
<mi>cov</mi>
<mrow>
<mo>(</mo>
<msub>
<mi>y</mi>
<mi>i</mi>
</msub>
<mo>,</mo>
<msub>
<mi>y</mi>
<mi>j</mi>
</msub>
<mo>)</mo>
</mrow>
<mo>=</mo>
<mi>k</mi>
<mrow>
<mo>(</mo>
<msub>
<mi>x</mi>
<mi>i</mi>
</msub>
<mo>,</mo>
<msub>
<mi>x</mi>
<mi>j</mi>
</msub>
<mo>)</mo>
</mrow>
<mo>+</mo>
<msubsup>
<mi>&sigma;</mi>
<mi>s</mi>
<mn>2</mn>
</msubsup>
<mi>&delta;</mi>
<mrow>
<mo>(</mo>
<msub>
<mi>x</mi>
<mi>i</mi>
</msub>
<mo>,</mo>
<msub>
<mi>x</mi>
<mi>j</mi>
</msub>
<mo>)</mo>
</mrow>
</mrow>
Wherein,Represent the sample variance of sampled point；L represents signal strength space correlation yardstick variable；Represent that Gauss makes an uproar
Sound；As i=j, δ (xi,yj) value be 1, be otherwise 0.
9. fingerprint base as claimed in claim 8 sets up device, it is characterised in that in model building module：
The degree of correlation between obtained signal strength values is calculated according to computing module, each point signal is obtained for sampling dot matrix X
Intensity matrix Y degree of correlation cov (Y)：
<mrow>
<mi>cov</mi>
<mrow>
<mo>(</mo>
<mi>Y</mi>
<mo>)</mo>
</mrow>
<mo>=</mo>
<mi>K</mi>
<mo>+</mo>
<msubsup>
<mi>&sigma;</mi>
<mi>s</mi>
<mn>2</mn>
</msubsup>
<mi>I</mi>
</mrow>
Wherein, K represents covariance matrix K [i, j]=k (x according to the sampling dot matrix X n × b tried to achievei,yj)；
And then obtain signal intensity profile model：
10. fingerprint base as claimed in claim 10 sets up device, it is characterised in that in computing module：
The signal intensity profile model set up according to model building module, any point in target area is calculated according to below equation
x*Signal strength values y*：
<mrow>
<mi>p</mi>
<mrow>
<mo>(</mo>
<msub>
<mi>y</mi>
<mo>*</mo>
</msub>
<mo>|</mo>
<msub>
<mi>x</mi>
<mo>*</mo>
</msub>
<mo>,</mo>
<mi>X</mi>
<mo>,</mo>
<mi>Y</mi>
<mo>)</mo>
</mrow>
<mo>=</mo>
<mi>N</mi>
<mrow>
<mo>(</mo>
<msub>
<mi>y</mi>
<mo>*</mo>
</msub>
<mo>;</mo>
<msub>
<mi>&mu;</mi>
<msub>
<mi>x</mi>
<mo>*</mo>
</msub>
</msub>
<mo>,</mo>
<msubsup>
<mi>&sigma;</mi>
<msub>
<mi>x</mi>
<mo>*</mo>
</msub>
<mn>2</mn>
</msubsup>
<mo>+</mo>
<msubsup>
<mi>&sigma;</mi>
<mi>s</mi>
<mn>2</mn>
</msubsup>
<mo>)</mo>
</mrow>
</mrow>
Wherein,K* is point x*With adopting
Sampling point matrix X covariance matrix, wherein, value k (i)=k (x in the covariance matrix*,xi)。
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