CN106102163A - WLAN fingerprint positioning method based on RSS linear correlation Yu secondary weighted centroid algorithm - Google Patents

Abstract

The invention discloses a kind of WLAN fingerprint positioning method based on RSS linear correlation Yu secondary weighted centroid algorithm.The present invention, firstly the need of setting up off-line fingerprint database, arranges Q reference point in target localizing environment, is organizing RSS sample more and is carrying out Filtering Processing, generate the fingerprint database describing described reference point reference point collection.Location can be implemented afterwards at described environment, calculate real-time RSS data and the linearly dependent coefficient of described fingerprint database sample data that user terminal receives, choose reference point corresponding to k correlation coefficient of maximum, use secondary weighted centroid algorithm to estimate the final position of user.The present invention can reduce the impact on indoor position accuracy of the terminal hardware difference, is effectively improved stability and the accuracy of indoor locating system.

Description

WLAN fingerprint positioning method based on RSS linear correlation Yu secondary weighted centroid algorithm

Technical field

The present invention relates to indoor positioning technologies field, be specifically related to one based on RSS linear correlation and secondary weighted barycenter The WLAN fingerprint positioning method of algorithm.

Background technology

Along with the fast development of the widely available of wireless network He mobile intelligent terminal, location Based service is come by more The most concerns, at emergency relief, health care, social networks, navigate and the field such as monitoring has obtained and has been widely applied and opens up Huge market prospect is shown.Due to the high coverage rate of WLAN hot spot service, people are to keeping whenever and wherever possible connecting wireless service Demand grow with each passing day, this makes many research institutions both domestic and external and company expand about WLAN location fingerprint indoor The research work of location technology, relevant application is the most at the early-stage, such as: the companies such as Google, Nokia, Baidu all exploitation and Improve different WLAN indoor locating systems.And the main method of indoor positioning has time of arrival (toa) (TOA), signal to arrive at present Reach time difference (TDOA), direction of arrival degree (AOA), received signal strength (RSS) etc., but owing to the interior space is narrower The modes such as little, the circulation way of radio wave is extremely complex, reflection, transmission, scattering make the more difficult reality of most localization method Existing.The localization method of received signal strength (RSS) refers to that the rule changed with propagation distance change according to signal intensity is come real Now positioning, it is affected by environment compared to other localization method less, and development cost is low, is therefore that the indoor of current main flow are fixed Method for position.

It is broadly divided into two stages: off-line fingerprint base establishment stage and tuning on-line based on RSS location fingerprint localization method Stage.In off-line fingerprint base acquisition phase, in needing to be spaced in area to be targeted according to a certain distance, gather several references Point, records and sets up wireless fingerprint data base by positional information corresponding for reference point and RSS information.The tuning on-line stage, RSS data sample according to user's Real-time Collection, estimates user current location in conjunction with location algorithm.But current RSS refers to The deficiency of stricture of vagina localization method various degrees: (1) owing to indoor environment existing the impact of the reasons such as multipath effect, AP's RSS value has bigger fluctuation, this bigger undulatory property can affect the stability of location, thus reduces the essence of indoor positioning Degree；(2), when fingerprint base is set up, the RSS sample data of a lot of acquired original is taken average and serves as sample point fingerprint, the most do not fill Divide and utilize the RSS sample information gathered, thus affect positioning precision；(3) due to the variability issues of terminal so that collection Finger print data has obvious difference, thus reduces positioning precision；(4), when determining position location coordinate, most algorithm uses Similar multiple sample points are averaged or weighted average, but k the similitude finally determined is random distribution mostly, some Point distance users position deviation is relatively big, uses traditional mode can affect positioning precision.

Summary of the invention

The invention aims to solve existing localization method RSS sample data do not make full use of, different mobile The problems such as terminal poor robustness, final position location estimation error are bigger, thus keep indoor locating system stability and Higher positioning precision.

In order to overcome above-mentioned the deficiencies in the prior art, the invention provides one based on RSS linear correlation with secondary weighted The WLAN fingerprint positioning method of centroid algorithm, specifically includes that

1. Filtering Processing RSS sample

Owing to there is the impact of the reasons such as multipath effect in indoor environment, the RSS value gathering AP has bigger fluctuation, in order to Improve the precision of location, need to be filtered original RSS value processing.This method takes amplitude limit average filter method, is equivalent to " limit filtration method "+" recurrence average filter method "；The new data every time sampled first carries out amplitude limiting processing, then data is passed It is bulldozed equal Filtering Processing.

First 1.1 carry out limit filtration process, the RSS sample data collecting described reference point, first predetermined permission Maximum deflection difference value ε (empirical value sets according to different local RSS robustness, typically takes between 5 to 10), calculate same AP The value of delta of adjacent sample data RSS_i=| rss_i,K,P-rss_j,K,P|, i=j+1, if δ_i> ε, then revise

${\mathrm{rss}}_{i,K,P}=\frac{1}{j}\underset{n=1}{\overset{j}{\Σ}}{\mathrm{rss}}_{n,K,P}$

δ_iRepresenting adjacent sample RSS difference, i and j represents RSS sample data group number, rss_i,K,PRepresent at described sampled point K The RSS value of P the AP that i & lt is measured；

RSS sample sequence after 1.2 pairs of limit filtration process process carries out recurrence average Filtering Processing again, obtaining continuously L sampled value regard a queue as, the length of queue is fixed as L, samples one group of new RSS data every time and puts into tail of the queue, and Throw away one group of RSS data (first in first out) of original head of the queue, L data in queue carried out arithmetic average computing:

${\mathrm{RSS}}_{K,n}=\frac{1}{L}\underset{i=1}{\overset{L}{\Σ}}{\mathrm{RSS}}_i$

Serve as this RSS sample by calculated RSS information, after having processed, obtain the N group RSS by Filtering Processing Sample sequence { s_K:RSS_K,1 RSS_K,2 … RSS_K,N}。

L represents the length of described filtering queue, it is contemplated that the RSS limited sample size that tuning on-line stage terminal receives, The span of general L is 3 to 5, RSS_iRepresent i-th group of RSS sample, RSS_K,NRepresent the N group RSS sample of reference point K.

2. the foundation of off-line fingerprint database

2.1 arrange Q reference point in target localizing environment, gather the RSS sample data of different AP in reference point, with ginseng As a example by examination point K, the RSS sample data collecting reference point K carries out amplitude limit average filter method Filtering Processing, finally gives N group By the RSS sample sequence { s of Filtering Processing_K:RSS_K,1 RSS_K,2 … RSS_K,N}；

K represents described reference point, and N represents the RSS sample group number collected by reference point K, s_KRepresent the RSS sample of reference point K This sequence, RSS_K,NRepresent the N group RSS sample of reference point K.

This sample sequence can be expressed as a matrix:

Wherein K is described reference point, and N is RSS sample group number, and P is AP quantity, rss_P,N,KRepresent described reference point K N group The RSS value of P AP in RSS information.

The RSS robustness of 2.2 calculating reference point K, the RSS variance of each AP of first calculating reference point K, then to required P variance carries out average value processing, and P refers to the AP quantity collected by described reference point K, utilizes this result of calculation as described reference The weight w of some K_K, the change of this reference point RSS value of the biggest expression of weights is the most violent.The computing formula of weights is as follows:

$\left\{\begin{array}{c}{w}_K=\frac{1}{PN}\underset{n=1}{\overset{N}{\Σ}}\underset{i=1}{\overset{P}{\Σ}}({\mathrm{rss}}_{i,n,K}-{\mathrm{\μ}}_{i,K})2\\ {\mathrm{\μ}}_K=\frac{1}{N}\underset{n=1}{\overset{N}{\Σ}}{\mathrm{RSS}}_{n,K}\end{array}\right.,n=1,2,...,M$

Wherein, P represents gathered AP number, and N is RSS sample group number, RSS_n,kRepresent n-th group in the sample of k-th position RSS sample, μ_KRepresent k-th position reference point RSS sample average.

2.3 for the linear dependence of Precise Representation fingerprint base sample Yu online RSS sample, to RSS_KThe every a line of matrix RSS numerical value arranges in descending order, i.e. can get new one group RSS matrix RSS'_K:

Matrix RSS'_KIn, each row element meets rss_i,j,k≥rss_i,j+1,k, i=1,2 ... R j=1,2 ... N-1. RSS'_KRepresent the RSS sample matrix processed in described reference point K, rss' through sequence_P,N,KRepresent described reference point K N group The RSS value of P AP in RSS information.

2.4 by RSS'_KIt is divided into M submatrix, often capable the averaging of each submatrix is obtainedByMatrix represents the fingerprint matrices information at described position K.

The RSS matrix dimensionality that the value of described M needs with tuning on-line stage terminal receives is identical, and experiment shows the increasing of M Contribute to greatly promoting the precision of location, it is contemplated that the real-time of location, the value of M is the most beneficial excessive, and general value is 3 to 6.RSS_M Represent m column RSS information in fingerprint matrices information.

3. calculate based on RSS linearly dependent coefficient

In 3.1 tuning on-line stages, terminal gathers the M group RSS sample sequence by Filtering Processing, is built into a P × M's RSS calculation matrix, a line every to RSS calculation matrix arranges in descending order, the i.e. available final online RSS matrix RSS needed^*:

Wherein M represents RSS sample group number, and P represents AP quantity,Represent the RSS of P AP in M group RSS sample Value.

3.2 are multiplied by the online terminal of reference point RSS weight computing according to Pearson correlation coefficients formula receives sample and fingerprint The linearly dependent coefficient r of data base's sample_K:

$r_K=\frac{\underset{i=1}{\overset{P}{\Σ}}\underset{j=1}{\overset{M}{\Σ}}({\mathrm{rss}}_{i,j,K}-{\mathrm{\μ}}_K)({\mathrm{rss}}_{i,j}^{*}-{\mathrm{\μ}}_{user})}{\sqrt{\underset{i=1}{\overset{P}{\Σ}}\underset{j=1}{\overset{M}{\Σ}}{({\mathrm{rss}}_{i,j,K}-{\mathrm{\μ}}_K)}^2}\sqrt{\underset{i=1}{\overset{P}{\Σ}}\underset{j=1}{\overset{M}{\Σ}}({\mathrm{rss}}_{i,j}^{*}-{\mathrm{\μ}}_{user})}}{w}_k,K=1,2,...,Q$

Wherein rss_i,j,KWithIt is illustrated respectively in what k-th reference point fingerprint and online terminal in fingerprint database received The numerical value of the i-th row jth row of RSS calculation matrix, w_kRepresent the weights of reference point, μ in fingerprint base_KRepresent K in fingerprint database The average of group fingerprint, μ_userRepresent the average of online RSS matrix.μ_KThe formula that is calculated as follows:

${\mathrm{\μ}}_K=\frac{1}{P\×M}\underset{i=1}{\overset{P}{\Σ}}\underset{j=1}{\overset{M}{\Σ}}{\mathrm{rss}}_{i,j,K}$

3.3, by after good for described Q Calculation of correlation factor, carry out descending sort to it, choose corresponding to front k correlation coefficient Location point { P₁ P₂ … P_k, P_k=(x_k y_k r_k)。

P_kRepresent the reference point information corresponding to kth correlation coefficient after described Q correlation coefficient descending sort, x_k y_k It is the coordinate figure corresponding to kth correlation coefficient respectively, r_kRepresenting kth correlation coefficient, in this method, the value of k should be greater than 3.

The most secondary weighted centroid algorithm location matches

4.1 by described P_kBecome two arrays VX and VY

$\left\{\begin{array}{cccccc}VX=\[{\mathrm{vx}}_1& {\mathrm{vx}}_2& ...& {\mathrm{vx}}_k\],& {\mathrm{vx}}_k=(x_k& r_k)\\ VY=\[{\mathrm{vy}}_1& {\mathrm{vy}}_2& ...& {\mathrm{vy}}_k\],& {\mathrm{vy}}_k=(y_k& r_k)\end{array}\right.$

VX and VY represents x coordinate array and y-coordinate array, vx in the reference point information corresponding to described k correlation coefficient_k And vy_kRepresent x coordinate and the y-coordinate information of kth correlation coefficient correspondence reference point.

4.2 pairs of described k nodes of locations utilize centroid algorithm to carry out for the first time and calculate, calculate unknown node I'(x'y')； Centroid algorithm is as follows:

$\begin{array}{cc}{x}^{\′}=\frac{\underset{i=1}{\overset{k}{\Σ}}{r}_i\×x_i}{\underset{i=1}{\overset{k}{\Σ}}{r}_i}& y^{\′}=\frac{\underset{i=1}{\overset{k}{\Σ}}{r}_i\×y_i}{\underset{i=1}{\overset{k}{\Σ}}{r}_i}\end{array}$

The reference point quantity that k obtains after representing described Q correlation coefficient descending sort, r_iRepresent that i-th reference point institute is right The correlation coefficient answered, x_i y_iRepresenting the location coordinate information that i-th reference point is corresponding respectively, x'y' represents the position tried to achieve respectively Coordinate information.

4.3 is random distribution due to k linear correlation nodes of locations, and the unknown node coordinate that first centroid algorithm calculates is by mistake Difference is bigger.In VX array and VY array, deduct the coordinate figure of unknown node with coordinate figure in array respectively and carry out absolute value Process obtains Φ_x,k=| x_k-x'|,Φ_y,k=| y_k-y'|, obtains α Φ of maximum_x,kAnd Φ_y,k, by corresponding coordinate information (value of α is relevant with k value, generally k/3) is deleted, available new number after delete processing in VX array with VY array Group VX' and VY'；

VX' and VY' array carries out second time weighted mass center algorithm calculate, obtain final positioning result I (x y).

Compared with prior art, the present invention has the following advantages:

The present invention RSS sample to gathering uses amplitude limit average filter method to be filtered processing, and effectively enhances AP's RSS Almost Sure Sample Stability；When setting up off-line fingerprint base, by gathering many group RSS sample datas, and to its carry out descending sort and Being grouped process of averaging, the fingerprint finally given can fully describe the RSS information of this location point；In order to avoid changing violent position Putting and be a little difficult to mate, the present invention is that each point calculates a description and changes the time the weights of intensity of variation, and applied to fixed In the algorithm of position；Owing to the present invention uses space similarity to realize location, although different terminals receives the ability of AP signal intensity Difference, but the curve of cyclical fluctuations of RSS is similar, and i.e. the RSS sample changed curve of different terminals collection is similar, therefore adopts Different terminals variability issues can be prevented effectively from by the present invention；K the analogous location point owing to finally determining is random mostly Distribution, some location point distance users current location deviation is relatively big, uses traditional mode can affect positioning precision, therefore this Bright give secondary weighted centroid algorithm to calculate the final position of user.

The present invention proposes to carry out indoor positioning, by substantial amounts of reality based on RSS linear correlation and secondary weighted centroid algorithm Demonstrate feasibility and the accuracy of the present invention, precision and the enhancing of indoor positioning can be effectively improved by the present invention The stability of location.

Accompanying drawing explanation

Fig. 1 is WLAN fingerprint positioning method based on RSS linear correlation Yu secondary weighted centroid algorithm of the present invention Flow chart；

Fig. 2 be terminal receive a certain AP many groups RSS sample Filtering Processing before and Filtering Processing after schematic diagram；

Fig. 3 be different terminals same position point accept filter process after difference in signal strength opposite sex schematic diagram；

Fig. 4 is schematic diagram based on secondary weighted centroid algorithm location matches.

Detailed description of the invention

In Fig. 1, describe flow process based on RSS linear correlation Yu the WLAN fingerprint positioning method of secondary weighted centroid algorithm Figure, provides detailed description below in conjunction with Fig. 1.

Step S1, the foundation of off-line fingerprint database

1.1RSS sample collection

In target localizing environment, arrange Q reference point, gather the RSS sample data of different AP in reference point, with reference As a example by some K, the RSS sample data collecting reference point K carries out amplitude limit average filter method Filtering Processing, finally gives N group and leads to Cross the RSS sample sequence { s of Filtering Processing_K:RSS_K,1 RSS_K,2 … RSS_K,N}；

K represents described reference point, and N represents the RSS sample group number collected by reference point K, s_KRepresent the RSS sample of reference point K This sequence, RSS_K,NRepresent the N group RSS sample of reference point K.

This sample sequence can be expressed as a matrix:

Wherein K is described reference point, and N is RSS sample group number, and P is AP quantity, rss_P,N,KRepresent described reference point K N group The RSS value of P AP in RSS information.

The RSS robustness of 1.2 calculating reference point K

The RSS variance of each AP of first calculating reference point K, then carries out average value processing to P required variance, and P refers to institute State the AP quantity collected by reference point K, utilize this result of calculation as the weight w of described reference point K_K, the biggest expression of weights The change of this reference point RSS value is the most violent.The computing formula of weights is as follows:

$\left\{\begin{array}{c}{w}_K=\frac{1}{PN}\underset{n=1}{\overset{N}{\Σ}}\underset{i=1}{\overset{P}{\Σ}}({\mathrm{rss}}_{i,n,K}-{\mathrm{\μ}}_{i,K})2\\ {\mathrm{\μ}}_K=\frac{1}{N}\underset{n=1}{\overset{N}{\Σ}}{\mathrm{RSS}}_{n,K}\end{array}\right.,n=1,2,...,M$

Wherein, P represents gathered AP number, and N is RSS sample group number, RSS_n,kRepresent n-th group in the sample of k-th position RSS sample, μ_KRepresent k-th position reference point RSS sample average.

1.3RSS sample process builds with fingerprint

For the linear dependence of Precise Representation fingerprint base sample Yu online RSS sample, to RSS_KMatrix every a line RSS number Value arranges in descending order, i.e. can get new one group RSS matrix RSS'_K:

Matrix RSS'_KIn, each row element meets

rss_i,j,k≥rss_i,j+1,k, i=1,2 ... R j=1,2 ... N-1

RSS'_KRepresent the RSS sample matrix processed in described reference point K, rss' through sequence_P,N,KRepresent described reference point The RSS value of P AP in K N group RSS information.

By RSS'_KIt is divided into M submatrix, often capable the averaging of each submatrix is obtained:

${\mathrm{RSS}}_K^{new}={\left[\begin{array}{cccc}{\mathrm{RSS}}_1& {\mathrm{RSS}}_2& ...& {\mathrm{RSS}}_M\end{array}\right]}_{P\×M}$

ByMatrix represents the fingerprint matrices information at described position K.

The RSS matrix dimensionality that the value of described M needs with tuning on-line stage terminal receives is identical, and experiment shows the increasing of M Contribute to greatly promoting the precision of location, it is contemplated that the real-time of location, the value of M is the most beneficial excessive, and general value is 3 to 6.RSS_M Represent m column RSS information in fingerprint matrices information.

Step S2, tuning on-line

2.1 build RSS calculation matrix

Terminal gathers the M group RSS sample sequence by Filtering Processing, is built into the RSS calculation matrix of a P × M, right The every a line of RSS calculation matrix arranges in descending order, the i.e. available final online RSS matrix RSS needed^*:

Wherein M represents RSS sample group number, and P represents AP quantity,Represent the RSS of P AP in M group RSS sample Value.

2.2 calculate dependency

It is multiplied by the online terminal of reference point RSS weight computing according to Pearson correlation coefficients formula and receives sample and finger print data The linearly dependent coefficient r of storehouse sample_K:

$r_K=\frac{\underset{i=1}{\overset{P}{\Σ}}\underset{j=1}{\overset{M}{\Σ}}({\mathrm{rss}}_{i,j,K}-{\mathrm{\μ}}_K)({\mathrm{rss}}_{i,j}^{*}-{\mathrm{\μ}}_{user})}{\sqrt{\underset{i=1}{\overset{P}{\Σ}}\underset{j=1}{\overset{M}{\Σ}}{({\mathrm{rss}}_{i,j,K}-{\mathrm{\μ}}_K)}^2}\sqrt{\underset{i=1}{\overset{P}{\Σ}}\underset{j=1}{\overset{M}{\Σ}}({\mathrm{rss}}_{i,j}^{*}-{\mathrm{\μ}}_{user})}}{w}_k,K=1,2,...,Q$

Wherein rss_i,j,KWithIt is illustrated respectively in what k-th reference point fingerprint and online terminal in fingerprint database received The numerical value of the i-th row jth row of RSS calculation matrix, w_kRepresent the weights of reference point, μ in fingerprint base_KRepresent K in fingerprint database The average of group fingerprint, μ_userRepresent the average of online RSS matrix.μ_KThe formula that is calculated as follows:

${\mathrm{\μ}}_K=\frac{1}{P\×M}\underset{i=1}{\overset{P}{\Σ}}\underset{j=1}{\overset{M}{\Σ}}{\mathrm{rss}}_{i,j,K}$

After good for described Q Calculation of correlation factor, it is carried out descending sort, choose corresponding to front k correlation coefficient Location point { P₁ P₂ … P_k, P_k=(x_k y_k r_k)。

P_kRepresent the reference point information corresponding to kth correlation coefficient after described Q correlation coefficient descending sort, x_k y_k It is the coordinate figure corresponding to kth correlation coefficient respectively, r_kRepresenting kth correlation coefficient, in this method, the value of k should be greater than 3.

2.3 secondary weighted value centroid methods carry out location matches

2.3.1 a centroid algorithm calculates

By described P_kBecome two arrays VX and VY

$\left\{\begin{array}{cccccc}VX=\[{\mathrm{vx}}_1& {\mathrm{vx}}_2& ...& {\mathrm{vx}}_k\],& {\mathrm{vx}}_k=(x_k& r_k)\\ VY=\[{\mathrm{vy}}_1& {\mathrm{vy}}_2& ...& {\mathrm{vy}}_k\],& {\mathrm{vy}}_k=(x_k& r_k)\end{array}\right.$

VX and VY represents x coordinate array and y-coordinate array, vx in the reference point information corresponding to described k correlation coefficient_k And vy_kRepresent x coordinate and the y-coordinate information of kth correlation coefficient correspondence reference point.

Described k nodes of locations utilizes centroid algorithm carry out for the first time calculate, calculate unknown node I'(x'y')；Barycenter Algorithm is as follows:

$\begin{array}{cc}{x}^{\′}=\frac{\underset{i=1}{\overset{k}{\Σ}}{r}_i\×x_i}{\underset{i=1}{\overset{k}{\Σ}}{r}_i}& y^{\′}=\frac{\underset{i=1}{\overset{k}{\Σ}}{r}_i\×y_i}{\underset{i=1}{\overset{k}{\Σ}}{r}_i}\end{array}$

The reference point quantity that k obtains after representing described Q correlation coefficient descending sort, r_iRepresent that i-th reference point institute is right The correlation coefficient answered, x_i y_iRepresenting the location coordinate information that i-th reference point is corresponding respectively, x'y' represents the position tried to achieve respectively Coordinate information.

2.3.2 two centroid algorithms calculate

Owing to k linear correlation nodes of locations is random distribution, the unknown node error of coordinate that first centroid algorithm calculates Bigger.In VX array and VY array, deduct the coordinate figure of unknown node with coordinate figure in array respectively and carry out at absolute value Reason obtains Φ_x,k=| x_k-x'|,Φ_y,k=| y_k-y'|, obtains α Φ of maximum_x,kAnd Φ_y,k, corresponding coordinate information is existed VX array deletes (value of α is relevant with k value, generally k/3) with in VY array, available new array after delete processing VX' and VY'；

VX' and VY' array carries out second time weighted mass center algorithm calculate, obtain final positioning result I (x y).

In step sl, as long as the RSS sample gathered is filtered processing, the present invention uses amplitude limit average filter method, Be equivalent to " limit filtration method "+" recurrence average filter method "；The new data every time sampled first carries out amplitude limiting processing, then by data Carry out recurrence average Filtering Processing.

Limit filtration processes: the RSS sample data collecting described reference point, the first predetermined maximum deflection difference value allowed ε (empirical value sets according to different local RSS robustness, typically takes between 5 to 10), calculates same AP adjacent sample data The value of delta of RSS_i=| rss_i,K,P-rss_j,K,P|, i=j+1, if δ_i> ε, then revise

${\mathrm{rss}}_{i,K,P}=\frac{1}{j}\underset{n=1}{\overset{j}{\Σ}}{\mathrm{rss}}_{n,K,P}$

δ_iRepresenting adjacent sample RSS difference, i and j represents RSS sample data group number, rss_i,K,PRepresent at described sampled point K The RSS value of P the AP that i & lt is measured；

Recurrence average Filtering Processing: the RSS sample sequence after processing limit filtration process carries out recurrence average filtering again Processing, L the sampled value obtained continuously is regarded as a queue, the length of queue is fixed as L, samples one group of new RSS every time Data put into tail of the queue, and throw away one group of RSS data (first in first out) of original head of the queue, and L data in queue are carried out Arithmetic average computing:

${\mathrm{RSS}}_{K,n}=\frac{1}{L}\underset{i=1}{\overset{L}{\Σ}}{\mathrm{RSS}}_i$

Serve as this RSS sample by calculated RSS information, after having processed, obtain the N group RSS by Filtering Processing Sample sequence { s_K:RSS_K,1 RSS_K,2 … RSS_K,N}。

L represents the length of described filtering queue, it is contemplated that the RSS limited sample size that tuning on-line stage terminal receives, The span of general L is 3 to 5, RSS_iRepresent i-th group of RSS sample, RSS_K,NRepresent the N group RSS sample of reference point K.

Effect after Filtering Processing is as shown in Figure 2.

In step s 2, the present invention proposes secondary weighted centroid algorithm and calculates for final position matching, is used for reducing matter Center algorithm calculates produced error, and the schematic diagram of secondary weighted centroid algorithm is as shown in Figure 4.It is analyzed Fig. 4 understanding, as Fruit replaces distance unknown node Q node to be farther out weighted barycenter computing with nearer node, can subtract to a certain extent The little impact the most first marking node, reduces position error further.Based on this thought, it is first according to standardized centroid algorithm first Secondary calculating, after trying to achieve barycenter, contrasts each nodes of locations X, Y distance calculating respectively, and removal X and Y is apart from farthest value respectively, Such that it is able to obtain a new nodes of locations.Calculate it is carried out secondary weighted mass center algorithm, try to achieve final estimation Location point.

Describe in figure 3 different mobile terminal (MT) same position point accept filter process after the difference in signal strength opposite sex show It is intended to, observes curve chart and find that the RSS sample changed trend of end the most of the same race reception is roughly the same, say, that they are linear phases Close.Make a concrete analysis of below:

To concrete AP, MT combination, it is assumed that P (d) and P (d₀) represent respectively with AP at a distance of any distance d and apart Reference distance d₀The received signal strength at place.According to Lognormal shadowing model, have:

$\[\frac{P\left(d\right)}{P\left(d_0\right)}\]=-10\mathrm{\β}log\left(\frac{d}{d_0}\right)+X_{dB}$

Wherein, Part I is path loss part (β is path-loss factor), and Part II is then that a normal state is random VariableAbove formula also can be write as:

$\begin{array}{c}P\left(d\right){|}_{dBm}=P\left(d_0\right){|}_{dBm}-10\mathrm{\β}log\left(\frac{d}{d_0}\right)+X_{dB}\\ =10\log \left(\frac{P_{AP}{G}_{AP}{G}_{MT}{\mathrm{\λ}}^2}{16{\mathrm{\π}}^2{d}_0^{2}l}\right)-10\mathrm{\β}\log \left(\frac{d}{d_0}\right)+X_{dB}\end{array}$

Wherein P_APIt it is the transmit power of AP；G_APIt it is the antenna gain of AP；G_MTIt it is the antenna gain of mobile terminal MT；L is to be System fissipation factor；λ is the wavelength of wireless signal.From formula, the RSS obtained at AP distance d depends on the hard of AP and MT Part parameter.

Two different terminals are as follows to the RSS observation of some AP at same position:

$P{\left(d\right)}_1{|}_{dBm}=10\log \left(\frac{P_{AP}{G}_{AP}{G}_{MT1}{\mathrm{\λ}}_{AP}^2}{16{\mathrm{\π}}^2{d}_0^2{l}_1}\right)-10{\mathrm{\β}}_1\log \left(\frac{d}{d_0}\right)+{\[X_1\]}_{dB}$

$P{\left(d\right)}_2{|}_{dBm}=10log\left(\frac{P_{AP}{G}_{AP}{G}_{MT2}{\mathrm{\λ}}_{AP}^2}{16{\mathrm{\π}}^2{d}_0^2{l}_2}\right)-10{\mathrm{\β}}_{2}log\left(\frac{d}{d_0}\right)+{\[X_2\]}_{dB}$

By P (d)₁-P(d)₂Can obtain:

$P{\left(d\right)}_1{|}_{dBm}-P{\left(d\right)}_2{|}_{dBm}=10log\left(\frac{G_{MT1}{l}_2}{G_{MT2}{l}_1}\right)-10log\left(\frac{d}{d_0}\right)({\mathrm{\β}}_1-{\mathrm{\β}}_2)+{\[X_1-X_2\]}_{dB}$

As can be seen from the above equation, different terminals RSS value is done after the recovery, result and AP configuration the most unrelated, although result is subject to To factor impacts such as terminal antenna gain G, system loss factor l, path-loss factor β, stochastic variable X, but work as different terminals Being under the ideal conditions that external condition is identical gathering RSS, these configurations are all to maintain constant, therefore, and the difference of different terminals Value is to tend to a constant.It follows that in the case of different terminals, the RSS sample value that each terminal is gathered is linear It is correlated with, as shown in Figure 3.Therefore the calculating during employing linear correlation carries out tuning on-line is to reduce to a certain extent The impact of different terminals.

From Fig. 3, it is also possible to analyzing when RSS is stronger, this dependency is obvious, when RSS is more weak, dependency Less obvious, therefore at positioning stage, it should carry out the selection of AP, abandon those more weak AP, be so favorably improved indoor Setting accuracy.

Claims (4)

1. WLAN fingerprint positioning method based on RSS linear correlation Yu secondary weighted centroid algorithm, it is characterised in that concrete steps As follows:

1) off-line fingerprint database establishment step:

A) in target localizing environment, arrange Q reference point, gather the RSS sample data of different AP in reference point, to reference point K The RSS sample data collected carries out amplitude limit average filter, obtains the N group RSS sample sequence by Filtering Processing, as follows:

{s_K:RSS_K,1 RSS_K,2 … RSS_K,N}

N represents the RSS sample group number collected by reference point K, s_KRepresent the RSS sample sequence of reference point K, RSS_K,NRepresent reference point The N group RSS sample of K；

B) the RSS robustness of described reference point K is calculated；The RSS variance of each AP of first calculating reference point K, then to required P Individual variance carries out average value processing, and P refers to the AP quantity collected by described reference point K, utilizes this result of calculation as described reference The weight w of some K_K, the change of this reference point RSS value of the biggest expression of weights is the most violent；

C) to described RSS sample sequence s_KBuild sample matrix RSS_K, to described RSS_KSample matrix every a line RSS numerical value is by fall Sequence arranges, and obtains matrix

Each row element meets rss_i,j,k≥rss_i,j+1,k, i=1,2 ... R j=1,2 ... N-1；

RSS'_KRepresent the RSS sample matrix processed in described reference point K, rss' through sequence_P,N,KRepresent described reference point K N The RSS value of P AP in group RSS information；

Matrix is divided into M submatrix, often capable the averaging of each submatrix is obtained ByMatrix represents the fingerprint matrices information at described position K；RSS_MRepresent m column RSS information in fingerprint matrices information；

2) tuning on-line realizes step:

2.1) linear dependence calculates:

A) terminal gathers the M group RSS sample sequence by Filtering Processing, is built into the RSS calculation matrix of a P × M, to RSS The every a line of calculation matrix arranges in descending order；

B) the linearly dependent coefficient r of computation and measurement matrix reference point fingerprint matrices different from fingerprint base_K；By Q correlation coefficient meter Calculate good after, it is carried out descending sort, chooses the location point { P corresponding to front k correlation coefficient₁ P₂ … P_k, P_k=(x_k y_k r_k)；

P_kRepresent the reference point information corresponding to kth correlation coefficient after described Q correlation coefficient descending sort, x_k y_kRespectively It is the coordinate figure corresponding to kth correlation coefficient, r_kRepresent kth correlation coefficient；

2.2) realization of secondary weighted centroid algorithm position matching:

A) by described P_kBecome two arrays VX and VY

$\left\{\begin{array}{cc}VX=\left[\begin{array}{cccc}{\mathrm{vx}}_1& {\mathrm{vx}}_2& ...& {\mathrm{vx}}_k\end{array}\right],& {\mathrm{vx}}_k=\left(\begin{array}{cc}{x}_k& r_k\end{array}\right)\\ VY=\left[\begin{array}{cccc}{\mathrm{vy}}_1& {\mathrm{vy}}_2& ...& {\mathrm{vy}}_k\end{array}\right],& {\mathrm{vy}}_k=\left(\begin{array}{cc}{y}_k& r_k\end{array}\right)\end{array}\right.$

VX and VY represents x coordinate array and y-coordinate array, vx in the reference point information corresponding to described k correlation coefficient_kAnd vy_k Represent x coordinate and the y-coordinate information of kth correlation coefficient correspondence reference point；

K nodes of locations utilizes centroid algorithm carry out for the first time calculate, calculate unknown node I'(x'y')；

B) in VX array and VY array, deduct the coordinate figure of unknown node with coordinate figure in array respectively and carry out at absolute value Reason obtains Φ_x,k=| x_k-x'|,Φ_y,k=| y_k-y'|, obtains α Φ of maximum_x,kAnd Φ_y,k, corresponding coordinate information is existed VX array and VY array are deleted, available new array VX' and VY' after delete processing；

VX' and VY' array carries out second time weighted mass center algorithm calculate, obtain final positioning result I (x y).

WLAN fingerprint positioning method based on RSS linear correlation Yu secondary weighted centroid algorithm the most according to claim 1, It is characterized in that, described amplitude limit average filter method that RSS sample data is carried out, specifically:

A) limit filtration process is first carried out, the RSS sample data that reference point is collected, the first predetermined maximum deviation allowed Value ε, calculates the value of delta of same AP adjacent sample data RSS_i=| rss_i,K,P-rss_j,K,P|, i=j+1, if δ_i> ε, then revise

${\mathrm{rss}}_{i,K,P}=\frac{1}{j}\underset{n=1}{\overset{j}{\Σ}}{\mathrm{rss}}_{n,K,P}$

δ_iRepresenting adjacent sample RSS difference, i and j represents RSS sample data group number, rss_i,K,PRepresent and survey in sampled point K i & lt The RSS value of P AP of amount；

B) the RSS sample sequence after processing limit filtration process carries out recurrence average Filtering Processing again, L obtained continuously Sampled value regards a queue as, and the length of queue is fixed as L, samples one group of new RSS data every time and puts into tail of the queue, and throws away former Carry out one group of RSS data of head of the queue, L data in queue carried out arithmetic average computing:

${\mathrm{RSS}}_{K,n}=\frac{1}{L}\underset{i=1}{\overset{L}{\Σ}}{\mathrm{RSS}}_i$

Serve as this RSS sample by calculated RSS information, after having processed, obtain the N group RSS sample by Filtering Processing Sequence { s_K:RSS_K,1 RSS_K,2 … RSS_K,N, wherein L represents the length of described filtering queue.

WLAN fingerprint positioning method based on RSS linear correlation Yu secondary weighted centroid algorithm the most according to claim 1, It is characterized in that, described linear dependence r_kIt is calculated as follows:

$r_K=\frac{\underset{i=1}{\overset{P}{\Σ}}\underset{j=1}{\overset{M}{\Σ}}({\mathrm{rss}}_{i,j,K}-{\mathrm{\μ}}_K)({\mathrm{rss}}_{i,j}^{*}-{\mathrm{\μ}}_{user})}{\sqrt{\underset{i=1}{\overset{P}{\Σ}}\underset{j=1}{\overset{M}{\Σ}}{({\mathrm{rss}}_{i,j,K}-{\mathrm{\μ}}_K)}^2}\sqrt{\underset{i=1}{\overset{P}{\Σ}}\underset{j=1}{\overset{M}{\Σ}}({\mathrm{rss}}_{i,j}^{*}-{\mathrm{\μ}}_{user})}}{w}_k,K=1,2,...,Q$

Wherein rss_i,j,KWithIt is illustrated respectively in the RSS that in fingerprint database, k-th reference point fingerprint receives with online terminal The numerical value of the i-th row jth row of calculation matrix, w_kRepresent the weights of reference point, μ in fingerprint base_kRepresent K group in fingerprint database The average of fingerprint, μ_userRepresent the average of online RSS matrix.

WLAN fingerprint positioning method based on RSS linear correlation Yu secondary weighted centroid algorithm the most according to claim 1, It is characterized in that, the secondary weighted centroid algorithm of described employing does final position matching, and the centroid algorithm used in twice calculating is public Formula:

$\begin{array}{cc}X=\frac{\underset{i=1}{\overset{k}{\Σ}}{r}_i\×x_i}{\underset{i=1}{\overset{k}{\Σ}}{r}_i}& Y=\frac{\underset{i=1}{\overset{k}{\Σ}}{r}_i\×y_i}{\underset{i=1}{\overset{k}{\Σ}}{r}_i}\end{array}$

The reference point quantity that k obtains after representing described Q correlation coefficient descending sort, r_iRepresent the phase corresponding to i-th reference point Close coefficient, x_i y_iRepresenting the location coordinate information that i-th reference point is corresponding respectively, X Y represents that the position finally tried to achieve is sat respectively Mark information.