CN102395195B - 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 nlos environment

Technical field

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

Background technology

In indoor positioning technology now, the location of mobile station method of estimation that people generally adopt is setting circle algorithm, a plurality of setting circle intersection points of demand solution or many positioning linear intersection points, but position error is very large; 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 a large amount of data samplings and data processing is set up required experience database in early stage; In addition, also have in processing signals loss model by experience with estimate in conjunction with etc., but this means when target is positioned, the position between travelling carriage be listed in to Consideration, on this basis, being multiplied of amount of calculation is inevitable.Based on these, consider, a kind of improved rotating signal subspace RSS (Rotate Signal Sub-space) algorithm based on location annulus algorithm is suggested.Simulation result shows, under the environment of 30 * 30 square metres, it is even less that the resulting positioning precision of this method can be controlled at 1.5 meters of left and right.

Summary of the invention

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

Technical scheme of the present invention is that a kind of method that improves indoor position accuracy under nlos environment, is characterized in that the method comprises the following steps:

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

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

Step 3: the overlapping region by 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 described deviations is:

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

Wherein:

RMSE is 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 described 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 path loss index, has indicated path loss advancing the speed with variable in distance;

D ₀for the center reference distance drawing by close-in measurement transmitter;

D is for sending the distance of separating between reception;

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

WAF is the partition wall factor;

ξ is envirment factor variable.

Described designation method is multiple regression analysis method.

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

Accompanying drawing explanation

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

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

The definite rectangle of annulus that Fig. 3 is is the center of circle by base station AP1 and AP2;

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 is only exemplary, rather than in order to limit the scope of the invention and to apply.

The present invention adopts Bahl and Padmanabhan signal transmission empirical model, considers the decay of the signal strength signal intensity causing 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 path loss index, has indicated path loss advancing the speed with variable in distance;

D ₀for the center reference distance drawing by close-in measurement transmitter;

D is for sending the distance of separating between reception;

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

WAF is the partition wall factor.

But in actual signal communication process, be subject to non line of sight (NLOS, Non Line of Sight) factor and the ambilateral impact of line loss, signal propagation model can produce an envirment factor variable ξ, makes signal propagation model have following variation:

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

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

In upper figure, affected by various error components, the measured value s that the empirical model transmitting via signal draws not is theoretical value but has certain positive and negative deviation.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 by Fig. 1, we can obtain d, s and s ₁, s ₂relational expression:

${(1+a)}^2{\mathrm{<figure-callout\; id="2"\; label="s"\; filenames="HDA0000102471390000011.png,HDA0000102471390000031.png"\; state="\{\{state\}\}">s</figure-callout>}}^2={\mathrm{<figure-callout\; id="2"\; label="s"\; filenames="HDA0000102471390000011.png,HDA0000102471390000031.png"\; state="\{\{state\}\}">s</figure-callout>}}_2^2\&GreaterEqual;{\mathrm{<figure-callout\; id="2"\; label="d"\; filenames="HDA0000102471390000011.png,HDA0000102471390000031.png"\; state="\{\{state\}\}">d</figure-callout>}}^2\&GreaterEqual;{\mathrm{<figure-callout\; id="1"\; label="s"\; filenames="HDA0000102471390000011.png,HDA0000102471390000031.png"\; state="\{\{state\}\}">s</figure-callout>}}_1^2={(1-a)}^2{s}^2---\left(3\right)$

The present invention enumerates the situation of three architecture location of mobile station.The in the situation that of three architectures, travelling carriage is bound to be positioned at three regions that annulus is crossing, as shown in Figure 2.The coordinate of travelling carriage should move in the region of black, can estimate with this roughly coordinate of travelling carriage.

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

By averaging, (2) formula becomes shape again as shown in (1) formula.For utilizing a plurality of independent samples to predict framing signal intensity, adopt Multiple Regression Analysis Method herein: utilize matrix below to replace formula (1):

P＝L×K (4)

Wherein:

$P=\left[\begin{array}{c}\mathrm{PL}\left(r_1\right)\\ \mathrm{PL}\left(r_2\right)\\ \&CenterDot;\\ \&CenterDot;\\ \&CenterDot;\\ \mathrm{PL}\left(r_N\right)\end{array}\right];L=\left[\begin{array}{ccc}1& -10\log ({\mathrm{<figure-callout\; id="1"\; label="r"\; filenames="HDA0000102471390000011.png,HDA0000102471390000031.png"\; state="\{\{state\}\}">r</figure-callout>}}_1/r_0)& -{\mathrm{<figure-callout\; id="1"\; label="l"\; filenames="HDA0000102471390000011.png,HDA0000102471390000031.png"\; state="\{\{state\}\}">l</figure-callout>}}_1\\ 1& -10\log ({\mathrm{<figure-callout\; id="2"\; label="r"\; filenames="HDA0000102471390000011.png,HDA0000102471390000031.png"\; state="\{\{state\}\}">r</figure-callout>}}_2/r_0)& -{\mathrm{<figure-callout\; id="2"\; label="l"\; filenames="HDA0000102471390000011.png,HDA0000102471390000031.png"\; state="\{\{state\}\}">l</figure-callout>}}_2\\ \&CenterDot;& \&CenterDot;& \&CenterDot;\\ \&CenterDot;& \&CenterDot;& \&CenterDot;\\ \&CenterDot;& \&CenterDot;& \&CenterDot;\\ 1& -10\log (r_N/r_0)& -l_N\end{array}\right];K=\left[\begin{array}{c}\mathrm{PL}\left(r_0\right)\\ \mathrm{\&alpha;}\\ \mathrm{WAF}\end{array}\right]$

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:

least-squares estimation for parameter vector K;

L ' is the transposed matrix of L.

Adopt Coefficient of determination R ²represent to return quality, definition:

Wherein:

for r _iloss of signal intensity in distance is estimated;

mean value for loss of signal intensity;

P _w(r _i) be r _iactual signal loss intensity in distance;

N is sample number.

Substitution is formula herein, has:

${\mathrm{<figure-callout\; id="2"\; label="R"\; filenames="HDA0000102471390000011.png,HDA0000102471390000031.png"\; state="\{\{state\}\}">R</figure-callout>}}^2=\frac{{\tilde{K}}^{\&prime;}{L}^{\&prime;}\text{P-N}{\stackrel{\&OverBar;}{P}}^2}{P^{\&prime;}P-N{\stackrel{\&OverBar;}{P}}^2},$ $\stackrel{\&OverBar;}{P}=\frac{1}{N}\underset{i=1}{\overset{N}{\mathrm{\&Sigma;}}}\mathrm{PL}\left(r_i\right)---\left(7\right)$

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 locating annulus algorithm shown in Fig. 2, estimate with the following method the position of travelling carriage: only consider base station AP ₁and AP ₂for the annulus in the center of circle, can obtain internal diameter minimum value r _{1_min}with external diameter maximum r _{2_max}, as follows:

$\left\{\begin{array}{c}{{\mathrm{<figure-callout\; id="1"\; label="r"\; filenames="HDA0000102471390000011.png,HDA0000102471390000031.png"\; state="\{\{state\}\}">r</figure-callout>}}_{1\_\mathrm{<figure-callout\; id="2"\; label="min"\; filenames="HDA0000102471390000011.png,HDA0000102471390000031.png"\; state="\{\{state\}\}">min</figure-callout>}}}^2={(x_A-x_1)}^2+{(y_A-y_1)}^2\\ {{\mathrm{<figure-callout\; id="2"\; label="r"\; filenames="HDA0000102471390000011.png,HDA0000102471390000031.png"\; state="\{\{state\}\}">r</figure-callout>}}_{2\_\mathrm{<figure-callout\; id="2"\; label="max"\; filenames="HDA0000102471390000011.png,HDA0000102471390000031.png"\; state="\{\{state\}\}">max</figure-callout>}}}^2={(x_A-x_2)}^2+{(y_A-y_2)}^2\end{array}\right.---\left(9\right)$

This equation has two groups of solution (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 respectively (x _b, y _b), (x _c, y _c) and (x _d, y _d), 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, as shown in Figure 3.

A selected length Δ xy, take it as a square of length of side work, with the rectangular area in this square traversing graph, when this foursquare center (x, y) meets inequality (13), square is included in and is determined in result.

$\left\{\begin{array}{c}{(x-x_1)}^2+{(y-y_1)}^2\&le;{\mathrm{<figure-callout\; id="1"\; label="r"\; filenames="HDA0000102471390000011.png,HDA0000102471390000031.png"\; state="\{\{state\}\}">r</figure-callout>}}_{1\_\max }\\ {(x-x_1)}^2+{(y-y_1)}^2\&GreaterEqual;{\mathrm{<figure-callout\; id="1"\; label="r"\; filenames="HDA0000102471390000011.png,HDA0000102471390000031.png"\; state="\{\{state\}\}">r</figure-callout>}}_{1\_\min }\\ {(x-x_2)}^2+{(y-y_2)}^2\&le;{\mathrm{<figure-callout\; id="2"\; label="r"\; filenames="HDA0000102471390000011.png,HDA0000102471390000031.png"\; state="\{\{state\}\}">r</figure-callout>}}_{2\_\max }\\ {(x-x_2)}^2+{(y-y_2)}^2\&GreaterEqual;{\mathrm{<figure-callout\; id="2"\; label="r"\; filenames="HDA0000102471390000011.png,HDA0000102471390000031.png"\; state="\{\{state\}\}">r</figure-callout>}}_{2\_\min }\\ {(x-x_3)}^2+{(y-y_3)}^2\&le;{\mathrm{<figure-callout\; id="3"\; label="r"\; filenames="HDA0000102471390000031.png,HDA0000102471390000032.png"\; state="\{\{state\}\}">r</figure-callout>}}_{3\_\max }\\ {(x-x_3)}^2+{(y-y_3)}^2\&GreaterEqual;{\mathrm{<figure-callout\; id="3"\; label="r"\; filenames="HDA0000102471390000031.png,HDA0000102471390000032.png"\; state="\{\{state\}\}">r</figure-callout>}}_{3\_\min }\end{array}\right.---\left(12\right)$

When square has traveled through after All Ranges, the coordinate result that note is included in is (x _i, y _i), i=1 wherein, 2 ... n.Now, (x _i, y _i) three base station AP of distance ₁, AP ₂, AP ₃distance be respectively d _1i, d _2iand d _3i, 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 (x, y)=(8,1), in the square area being surrounded, has 3 base station: AP 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.

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

In Fig. 5, (a), (c), be (e) respectively to work as r _measure> estimates _{meter meter}time, utilize and be related to s ₁=a value that (1+a%) s draws; And (b), (d), be (f) to work as r _measure< r _estimatetime, utilize and be related to s ₂=a value that (1-a%) s draws.From Fig. 5, can obtain the roughly result of a: for AP ₁: a=15; For AP ₂: a=20; For AP ₃: a=6.

Statistics by the deviation ratio a that obtains above, positions emulation to above-mentioned location model.Suppose that the positioning result simulating is (x, y), and travelling carriage actual coordinate is (x _m, y _m), deviations (RMSE) can calculate by through type (16) so:

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

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 within the scope of this is carried out quantitative statistics, and Fig. 6 is its histogrammic visual representation.

Table 1 improves the error statistics result of location model:

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
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 the localizing environment of indoor 30 * 30 square metres, compared with the existing setting circle model generally adopting, adopt the positioning precision of the improvement setting circle ring model of same algorithm to improve more than 3 meters.Analysis shows, this improved RSS location algorithm, under the condition of existing algorithm complex and equipment precision, is a kind of effective indoor orientation method.

The above; be only the present invention's embodiment preferably, but protection scope of the present invention is not limited to this, is anyly familiar with in technical scope that those skilled in the art disclose in the present invention; the variation that can expect easily or replacement, within all should being encompassed in 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 (3)

1. improve a method for indoor position accuracy under nlos environment, it is characterized in that the method comprises the following steps:

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

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

Step 3: by the overlapping region between the annulus of location, draw the estimation region of travelling carriage;

Step 4: on the basis of step 3, the transmission path loss of signal is averaged, and with specifying Multiple Regression Analysis Method the signal strength signal intensity of travelling carriage to be estimated to obtain the locating area of travelling carriage.

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

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

Wherein:

RMSE is 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 nlos environment according to claim 1, is characterized in that the computing formula of the transmission path loss of described 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 path loss index, has indicated path loss advancing the speed with variable in distance;

D ₀for the center reference distance drawing by close-in measurement transmitter;

D is for sending the distance of separating between reception;

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

WAF is the partition wall factor;

ξ is envirment factor variable.

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