CN102395195A - Method for raising indoor positioning precision under non-line-of-sight environment - Google Patents

Abstract

The invention discloses a method for raising indoor positioning precision under a non-line-of-sight environment in the wireless positioning technology field. The method comprises the following steps: based on a signal transmission experience model, through adding a baffle wall factor and an environment factor, a transmission model of a signal is obtained; calculating positioning deviation of a signal of a mobile station relative to a fixed base station, and positioning rings of the signal of the mobile station corresponding to the fixed base station are obtained; through an overlapped area of the positioning rings, an estimation area of the mobile base station is obtained; calculating an average value of transmission path loss of the signal, and using an appointed method to estimate signal intensity of the mobile station to obtain a positioning area of the mobile station. According to the invention, on the basis of ensuring a low amount of calculation, precision of indoor positioning is raised; simultaneously, influence of a wall on a positioning signal is considered, the baffle wall factor is taken into consideration, and a feasible degree of an algorithm is raised.

Description

A kind of method that improves indoor position accuracy under the nlos environment

Technical field

The invention belongs to the wireless location technology field, relate in particular to a kind of method that improves indoor position accuracy under the nlos environment.

Background technology

In indoor positioning technology now, the location of mobile station method of estimation that people generally adopt is the setting circle algorithm, and demand is separated a plurality of setting circle intersection points or many location straight-line intersections, but position error is very big; Also have from range measurement and start with, the other factorses such as statistical information of range measurement are taken into account, it certainly will will carry out in early stage, and lot of data is sampled and data processing is set up required experience database; In addition, also have in the processing signals loss model experience is combined with estimation etc., but this means that when target is positioned list the position between the travelling carriage in Consideration, on this basis, being multiplied of amount of calculation is inevitable.Consider that based on these a kind of improved rotating signal subspace RSS (Rotate Signal Sub-space) algorithm based on location annulus algorithm is suggested.Simulation result shows, under 30 * 30 square metres environment, the resulting positioning accuracy of this method can be controlled at about 1.5 meters even be littler.

Summary of the invention

The big deficiency that waits of position error to the setting circle algorithm of mentioning in the above-mentioned background technology the present invention proposes a kind of method that improves indoor position accuracy under the nlos environment.

Technical scheme of the present invention is that a kind of method that improves indoor position accuracy under the nlos environment is characterized in that this method may further comprise the steps:

Step 1: on the basis of signal transmission empirical model,, obtain the mode of signal through increasing the partition wall factor and envirment factor;

Step 2: on the basis of the mode of signal, calculate the deviations of the signal of travelling carriage, and then obtain the location annulus of the signal of travelling carriage corresponding to fixed base stations corresponding to fixed base stations;

Step 3: the overlapping region through between the annulus of location draws the estimation region of mobile base station;

Step 4: on the basis of step 3, the transmission path loss of signal is averaged, and the signal strength signal intensity of travelling carriage is estimated to obtain the locating area of travelling carriage with designation method.

The computing formula of said deviations is:

$\mathrm{RMSE}=\sqrt{{(x-x_m)}^2+{(y-y_m)}^2}$

Wherein:

RMSE is a deviations;

x _mActual abscissa for travelling carriage;

y _mActual ordinate for travelling carriage;

X is the abscissa of the travelling carriage that simulates;

Y is the ordinate of the travelling carriage that simulates.

The computing formula of the transmission path loss of said signal is:

$\mathrm{PL}\left(d\right)=\mathrm{PL}\left(d_0\right)+10n\log \left(\frac{d}{d_0}\right)-l\·\mathrm{WAF}+\mathrm{\ξ}$

Wherein:

PL () is the transmission path loss of signal;

N is a path loss index, has indicated path loss advancing the speed with variable in distance;

d ₀Be the center reference distance that draws through the close-in measurement transmitter;

D is for sending the distance of separating between the reception;

L is the partition wall number between travelling carriage and the base station;

WAF is the partition wall factor;

ξ is the envirment factor variable.

Said designation method is the multiple regression analysis method.

The present invention adopts the empirical model of signal transmission to study, and has guaranteed the generality of algorithm.The method that has adopted theory analysis, feasibility study and Computer Simulation to combine has been verified the scheme that is proposed from the theory and practice aspect.The present invention can improve the precision of indoor positioning on the basis that guarantees low amount of calculation; Considered the influence of wall simultaneously, listed the partition wall factor in limit of consideration, improved the feasibility of algorithm framing signal.

Description of drawings

Fig. 1 is based on the location model of signal strength signal intensity under the NLOS condition;

Fig. 2 is based on the location model of signal strength signal intensity under the three base station NLOS;

Fig. 3 is for by base station AP1 and AP2 being the definite rectangle of annulus in the center of circle;

Fig. 4 is that orientation range and AP distribute;

Fig. 5 is that the probability of occurrence of deviation ratio a distributes;

Fig. 6 is for improving the error statistics result of location model.

Embodiment

Below in conjunction with accompanying drawing, preferred embodiment is elaborated.Should be emphasized that following explanation only is exemplary, rather than in order to limit scope of the present invention and application thereof.

The present invention adopts Bahl and Padmanabhan signal transmission empirical model, considers the attenuation of signal intensity that causes because of the partition wall factor between travelling carriage (MS) and base station (BS).

$\mathrm{PL}\left(d\right)=\mathrm{PL}\left(d_0\right)+10n\log \left(\frac{d}{d_0}\right)-l\·\mathrm{WAF}---\left(1\right)$

Wherein:

PL () is the transmission path loss of signal;

N is a path loss index, has indicated path loss advancing the speed with variable in distance;

d ₀Be the center reference distance that draws through the close-in measurement transmitter;

D is for sending the distance of separating between the reception;

L is the partition wall number between travelling carriage and the base station;

WAF is the partition wall factor.

But in the actual signal communication process, receive non line of sight (NLOS, Non Line of Sight) factor and the ambilateral influence of line loss, signal propagation model can produce an envirment factor variable ξ, makes signal propagation model that following variation arranged:

$\mathrm{PL}\left(d\right)=\mathrm{PL}\left(d_0\right)+10n\log \left(\frac{d}{d_0}\right)-l\·\mathrm{WAF}+\mathrm{\ξ}---\left(2\right)$

The signal power that the non line of sight NLOS factor makes receiving terminal receive possibly strengthen also and possibly weaken.A kind of in this case new geometrical model design is as shown in Figure 1.Middle dotted line performance theoretical value, and solid line representes to record the threshold value of numerical value, recording data can internal diameter be s in diagram ₁With external diameter be s ₂Circle ring area in change.

Among the last figure, influenced by various error components, the measured value s that the empirical model that transmits via signal draws not is theoretical value but certain positive and negative deviation is arranged.On the other hand, according to the regularity of distribution of error, extent of deviation is limited.Therefore, we establish a is deviation ratio, i.e. maximum deviation degree, and then by Fig. 1, we can obtain d, s and s ₁, s ₂Relational expression:

${(1+a)}^2{\mathrm{<figure-callout\; id="2"\; label="s"\; filenames="HDA0000102471390000031.png,HDA0000102471390000032.png"\; state="\{\{state\}\}">s</figure-callout>}}^2={\mathrm{<figure-callout\; id="2"\; label="s"\; filenames="HDA0000102471390000031.png,HDA0000102471390000032.png"\; state="\{\{state\}\}">s</figure-callout>}}_2^2\&GreaterEqual;{\mathrm{<figure-callout\; id="2"\; label="d"\; filenames="HDA0000102471390000031.png,HDA0000102471390000032.png"\; state="\{\{state\}\}">d</figure-callout>}}^2\&GreaterEqual;{\mathrm{<figure-callout\; id="1"\; label="s"\; filenames="HDA0000102471390000031.png,HDA0000102471390000032.png"\; state="\{\{state\}\}">s</figure-callout>}}_1^2={(1-a)}^2{s}^2---\left(3\right)$

The present invention enumerates the situation of positioning mobile station position, three base stations.Under the situation of location, three base stations, travelling carriage is bound to be positioned at the zone that three annulus intersect, and is as shown in Figure 2.The coordinate of travelling carriage should move in the zone of black, can estimate the roughly coordinate of travelling carriage with this.

Since the existence of envirment factor variable ξ, variable PL (d ₀) and d do not belong to dependency relation, therefore can't obtain PL (d through the simultaneous of equation ₀) and n.But in communication channel, particularly in the Gaussian channel, repeatedly the mean value of transmission environment factor variable ξ is 0, and the present invention takes the average method of laggard line retrace analysis of the transmission path loss of signal eliminate is disturbed and signal strength signal intensity is estimated:

Through averaging, (2) formula becomes shape again shown in (1) formula.To utilizing a plurality of independent samples that framing signal intensity is predicted, this paper adopts the multiple regression analysis method: the matrix below utilizing replaces formula (1):

P＝L×K (4)

Wherein:

$P=\left[\begin{array}{c}\mathrm{PL}\left(r_1\right)\\ \mathrm{PL}\left(r_2\right)\\ \&CenterDot;\\ \&CenterDot;\\ \&CenterDot;\\ \mathrm{PL}\left(r_N\right)\end{array}\right];L=\left[\begin{array}{ccc}1& -10\log ({\mathrm{<figure-callout\; id="1"\; label="r"\; filenames="HDA0000102471390000031.png,HDA0000102471390000032.png"\; state="\{\{state\}\}">r</figure-callout>}}_1/r_0)& -{\mathrm{<figure-callout\; id="1"\; label="l"\; filenames="HDA0000102471390000031.png,HDA0000102471390000032.png"\; state="\{\{state\}\}">l</figure-callout>}}_1\\ 1& -10\log ({\mathrm{<figure-callout\; id="2"\; label="r"\; filenames="HDA0000102471390000031.png,HDA0000102471390000032.png"\; state="\{\{state\}\}">r</figure-callout>}}_2/r_0)& -{\mathrm{<figure-callout\; id="2"\; label="l"\; filenames="HDA0000102471390000031.png,HDA0000102471390000032.png"\; state="\{\{state\}\}">l</figure-callout>}}_2\\ \&CenterDot;& \&CenterDot;& \&CenterDot;\\ \&CenterDot;& \&CenterDot;& \&CenterDot;\\ \&CenterDot;& \&CenterDot;& \&CenterDot;\\ 1& -10\log (r_N/r_0)& -l_N\end{array}\right];K=\left[\begin{array}{c}\mathrm{PL}\left(r_0\right)\\ \mathrm{\&alpha;}\\ \mathrm{WAF}\end{array}\right]$

The least-squares estimation of parameter vector K is provided by following formula:

$\tilde{K}={\left(L^{\&prime;}L\right)}^{-1}{L}^{\&prime;}P---\left(5\right)$

Wherein:

is the least-squares estimation of parameter vector K;

L ' is the transposed matrix of L.

Adopt and confirm coefficients R ²Expression returns quality, definition:

Wherein:

Be r _iThe interior loss of signal intensity of distance is estimated;

is the mean value of loss of signal intensity;

P _W(r _i) be r _iThe actual signal loss intensity that distance is interior;

N is a sample number.

Substitution this paper formula has:

${\mathrm{<figure-callout\; id="2"\; label="R"\; filenames="HDA0000102471390000031.png,HDA0000102471390000032.png"\; state="\{\{state\}\}">R</figure-callout>}}^2=\frac{{\tilde{K}}^{\&prime;}{L}^{\&prime;}\text{P-N}{\stackrel{\&OverBar;}{P}}^2}{P^{\&prime;}P-N{\stackrel{\&OverBar;}{P}}^2},$ $\stackrel{\&OverBar;}{P}=\frac{1}{N}\underset{i=1}{\overset{N}{\mathrm{\&Sigma;}}}\mathrm{PL}\left(r_i\right)---\left(7\right)$

The standard deviation sigma of prediction signal loss Strength Changes _PFor:

${\mathrm{\&sigma;}}_P=\sqrt{\frac{1}{N}(P^{\&prime;}-{\tilde{K}}^{\&prime;}{L}^{\&prime;})P}---\left(8\right)$

Wherein:

P ' is the transposed matrix of P.

For location shown in Figure 2 annulus algorithm, estimate the position of travelling carriage with following method: only consider base station AP ₁And AP ₂Be the annulus in the center of circle, can get internal diameter minimum value r _{1_min}With external diameter maximum r _{2_max}, as follows:

$\left\{\begin{array}{c}{{\mathrm{<figure-callout\; id="1"\; label="r"\; filenames="HDA0000102471390000031.png,HDA0000102471390000032.png"\; state="\{\{state\}\}">r</figure-callout>}}_{1\_\mathrm{<figure-callout\; id="2"\; label="min"\; filenames="HDA0000102471390000031.png,HDA0000102471390000032.png"\; state="\{\{state\}\}">min</figure-callout>}}}^2={(x_A-x_1)}^2+{(y_A-y_1)}^2\\ {{\mathrm{<figure-callout\; id="2"\; label="r"\; filenames="HDA0000102471390000031.png,HDA0000102471390000032.png"\; state="\{\{state\}\}">r</figure-callout>}}_{2\_\mathrm{<figure-callout\; id="2"\; label="max"\; filenames="HDA0000102471390000031.png,HDA0000102471390000032.png"\; state="\{\{state\}\}">max</figure-callout>}}}^2={(x_A-x_2)}^2+{(y_A-y_2)}^2\end{array}\right.---\left(9\right)$

This equation has two groups to separate (x _A1, y _A1) and (x _A2, y _A2), get here near base station AP ₃That group separate, that is:

(x _A，y _A)＝argminf(x _Ai，y _Ai)＝[(x _Ai-x ₃) ²-(y _Ai-y ₃) ²]?i＝1，2(10)

In like manner, can obtain (x respectively _B, y _B), (x _C, y _C) and (x _D, y _D), then order:

$\left\{\begin{array}{c}{x}_{\min }=\min (x_A,x_B,x_C,x_D)\\ x_{\max }=\max (x_A,x_B,x_C,x_D)\\ y_{\min }=\min (y_A,y_B,y_C,y_D)\\ y_{\max }=\max (y_A,y_B,y_C,y_D)\end{array}\right.---\left(11\right)$

Can form four point (x _Min, y _Min), (x _Max, y _Min), (x _Max, y _Max) and (x _Min, y _Max), they form a rectangle in the drawings, and are as shown in Figure 3.

Selected length Δ xy is that the length of side is made a square with it, and with the rectangular area in this square traversing graph, (x y) meets inequality (13), then square is included in and is confirmed among the result when this foursquare center.

$\left\{\begin{array}{c}{(x-x_1)}^2+{(y-y_1)}^2\&le;{\mathrm{<figure-callout\; id="1"\; label="r"\; filenames="HDA0000102471390000031.png,HDA0000102471390000032.png"\; state="\{\{state\}\}">r</figure-callout>}}_{1\_\max }\\ {(x-x_1)}^2+{(y-y_1)}^2\&GreaterEqual;{\mathrm{<figure-callout\; id="1"\; label="r"\; filenames="HDA0000102471390000031.png,HDA0000102471390000032.png"\; state="\{\{state\}\}">r</figure-callout>}}_{1\_\min }\\ {(x-x_2)}^2+{(y-y_2)}^2\&le;{\mathrm{<figure-callout\; id="2"\; label="r"\; filenames="HDA0000102471390000031.png,HDA0000102471390000032.png"\; state="\{\{state\}\}">r</figure-callout>}}_{2\_\max }\\ {(x-x_2)}^2+{(y-y_2)}^2\&GreaterEqual;{\mathrm{<figure-callout\; id="2"\; label="r"\; filenames="HDA0000102471390000031.png,HDA0000102471390000032.png"\; state="\{\{state\}\}">r</figure-callout>}}_{2\_\min }\\ {(x-x_3)}^2+{(y-y_3)}^2\&le;{\mathrm{<figure-callout\; id="3"\; label="r"\; filenames="HDA0000102471390000031.png,HDA0000102471390000032.png"\; state="\{\{state\}\}">r</figure-callout>}}_{3\_\max }\\ {(x-x_3)}^2+{(y-y_3)}^2\&GreaterEqual;{\mathrm{<figure-callout\; id="3"\; label="r"\; filenames="HDA0000102471390000031.png,HDA0000102471390000032.png"\; state="\{\{state\}\}">r</figure-callout>}}_{3\_\min }\end{array}\right.---\left(12\right)$

After square had traveled through All Ranges, the coordinate result that note is included in was (x _i, y _i), i=1 wherein, 2 ... N.At this moment, (x _i, y _i) three base station AP of distance ₁, AP ₂, AP ₃Distance be respectively d _1i, d _2iAnd d _3i, then according to the location algorithm of weighted mass center, the position coordinates of travelling carriage is:

$x=\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}[x_i/(d_{1i}+d_{2i}+d_{3i})]/\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}[1/(d_{1i}+d_{2i}+d_{3i})]---\left(13\right)$

$y=\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}[y_i/(d_{1i}+d_{2i}+d_{3i})]/\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}[1/(d_{1i}+d_{2i}+d_{3i})]---\left(14\right)$

The physical location of supposing travelling carriage (MS) is for (x y)=(8,1), has 3 base station: AP in the square area that is surrounded by (15,15), (15 ,-15), (15 ,-15) and (15,15) ₁: (15,15), AP ₂(15 ,-15) and AP ₃(0,0), as shown in Figure 4.

At first, the location model parameter based on signal strength signal intensity under the NLOS is estimated, and continued emulation on this basis and finally obtain positioning result through linear regression.In simulation process, need to confirm deviation ratio a and estimate range information from performance number through the use experience signal propagation model.In addition, for reducing the channel errors in the communication process, this programme has adopted the received power several times, and the processing method of distance estimations is carried out in average back at the substitution model.At first carry out the estimation of deviation ratio a below: this programme carries out emulation under indoor 30 * 30 square metres localizing environment, and 1000 times distance estimations has been carried out in emulation, and statistical error production is then confirmed the upper limit of error.

Among Fig. 5, (a), (c), be respectively to work as r (e) _Measure＞estimate _{The meter meter}The time, utilize to concern s ₁=(1+a%) a value that draws of s; And (b), (d), then be to work as r (f) _Measure＜r _EstimateThe time, utilize to concern s ₂=(1-a%) a value that draws of s.Can obtain the roughly result of a from Fig. 5: for AP ₁: a=15; For AP ₂: a=20; For AP ₃: a=6.

Statistics through the above deviation ratio a that obtains positions emulation to above-mentioned location model.The positioning result of supposing to simulate is for (x, y), and the travelling carriage actual coordinate is (x _m, y _m), so deviations (RMSE) but through type (16) calculate:

$\mathrm{RMSE}=\sqrt{{(x-x_m)}^2+{(y-y_m)}^2}---\left(15\right)$

Through above analytical calculation, can obtain the simulation result of RMSE in 1000 location.This result of number of times that table 1 pair error appears in this scope carries out quantitative statistics, and Fig. 6 then is its histogrammic visual representation.

Table 1 improves the error statistics result of location model:

The RMSE scope	[0，0.5]	[0.5，1]	[1，1.5]	[1.5，2]	[2，2.5]
						Number of times	96	145	281	312	133
The RMSE scope	[2.5，3]	[3，3.5]	[3.5，4]	[4，4.5]	[4.5，5]
						Number of times	24	2	1	1	5

As can be seen from the figure, under indoor 30 * 30 square metres localizing environment,, adopt the positioning accuracy of the improvement setting circle ring model of same algorithm to improve more than 3 meters compared with the existing setting circle model that generally adopts.Analysis shows that this improved RSS location algorithm is a kind of effective indoor orientation method under the condition of existing algorithm complexity and equipment precision.

The above; Be merely the preferable embodiment of the present invention, but protection scope of the present invention is not limited thereto, any technical staff who is familiar with the present technique field is in the technical scope that the present invention discloses; The variation that can expect easily or replacement all should be encompassed within protection scope of the present invention.Therefore, protection scope of the present invention should be as the criterion with the protection range of claim.

Claims (4)

1. method that improves indoor position accuracy under the nlos environment is characterized in that this method may further comprise the steps:

Step 1: on the basis of signal transmission empirical model,, obtain the mode of signal through increasing the partition wall factor and envirment factor;

Step 2: on the basis of the mode of signal, calculate the deviations of the signal of travelling carriage, and then obtain the location annulus of the signal of travelling carriage corresponding to fixed base stations corresponding to fixed base stations;

Step 3: the overlapping region through between the annulus of location draws the estimation region of mobile base station;

Step 4: on the basis of step 3, the transmission path loss of signal is averaged, and the signal strength signal intensity of travelling carriage is estimated to obtain the locating area of travelling carriage with designation method.

2. a kind of method that improves indoor position accuracy under the nlos environment according to claim 1 is characterized in that the computing formula of said deviations is:

$\mathrm{RMSE}=\sqrt{{(x-x_m)}^2+{(y-y_m)}^2}$

Wherein:

RMSE is a deviations;

x _mActual abscissa for travelling carriage;

y _mActual ordinate for travelling carriage;

X is the abscissa of the travelling carriage that simulates;

Y is the ordinate of the travelling carriage that simulates.

3. a kind of method that improves indoor position accuracy under the nlos environment according to claim 1 is characterized in that the computing formula of the transmission path loss of said signal is:

$\mathrm{PL}\left(d\right)=\mathrm{PL}\left(d_0\right)+10n\log \left(\frac{d}{d_0}\right)-l\&CenterDot;\mathrm{WAF}+\mathrm{\&xi;}$

Wherein:

PL () is the transmission path loss of signal;

N is a path loss index, has indicated path loss advancing the speed with variable in distance;

d ₀Be the center reference distance that draws through the close-in measurement transmitter;

D is for sending the distance of separating between the reception;

L is the partition wall number between travelling carriage and the base station;

WAF is the partition wall factor;

ξ is the envirment factor variable.

4. a kind of method that improves indoor position accuracy under the nlos environment according to claim 1 is characterized in that said designation method is the multiple regression analysis method.

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