CN105828435A - Distance correction weighted centroid localization method based on reception signal intensity optimization - Google Patents

Abstract

The invention relates to a distance correction weighted centroid localization method based on reception signal intensity optimization and aims to realize precise localization through moving an unknown node in a complex indoor environment. The method comprises steps that RSSI signal processing optimization strategies are comprised, that is, a Gauss mean value mixing filtering model is utilized to optimize an RSSI value, a problem of severe jittering existing in the RSSI value is solved, and a more reliable and reasonable RSSI value is acquired; timely beacon node combination is carried out, that is, the smallest four nodes are selected by utilizing a bubble sorting method from small to large to carry out combination localization; whether two circles in the combination intersect is determined, if not, a distance correction scheme is utilized to carry out optimization, and fault tolerance capability, adaptability and localization precision of the localization algorithm are improved; a four-side weighted centroid localization algorithm is utilized to carry out timely localization, and the position information of the unknown node is acquired. Through the method, localization precision is improved through the optimized localization method, stronger adaptability and higher fault tolerance capability are realized, and the method is suitable for realizing application and popularization in the complex indoor environment.

Description

The distance correction weighted mass center localization method optimized based on received signal strength

Technical field

The present invention relates to a kind of distance correction weighted mass center localization method.Particularly relate to a kind of distance correction weighted mass center localization method optimized based on received signal strength optimizing rssi measurement value under complex indoor environment and improving weighted mass center location.

Background technology

Along with the growth rapidly of information, how to process big data and how to provide reliable information to become key.Information is enclosed location label and has become a kind of the most frequently used processing mode.Popular along with mobile device and personal device, location becomes popular.Alignment system can interpolate that the positional information of equipment, and positional information is used for service based on location simultaneously, as navigated, following the tracks of, monitoring etc..GPS system can provide outdoor individual location information easily, but GPS relies on the transmission of signal (line-of-sight) between satellite and recipient, and under indoor situations, the decay of signal causes the unreliable of GPS location information.Daily life and work are in indoor mostly, along with mobile Internet and the fast development of smart machine, indoor positioning technologies becomes the bottleneck of the application such as O2O, Smart Home, Indoor Robot, and application based on indoor positioning technologies has urgent demand and is widely applied prospect.

The indoor positioning technologies of main flow can be divided into two big classes at present: based on the non-ranging and location algorithm of range finding.The former is mainly adjusted the distance estimated by internodal connectedness and a plurality of route, higher to hardware requirement, mainly has centroid algorithm, DV-Hop algorithm, approximates three limits interior angle testing algorithm (APIT) etc.；Algorithm based on range finding is mainly by measuring the information such as the distance of adjacent sensors node, orientation angles, use the location algorithm founding mathematical models such as trilateration, triangulation, maximal possibility estimation, estimate node location, thus obtain the actual position information of unknown node.It is merely resting on theoretical research stage based on non-ranging location algorithm, and mostly carry out under simulated environment, need to assume a lot of uncertain factor, and these factors tend not to meet in actual applications, the most generally use location algorithm.Frequently with ranging technology include RSSI (receivedsignalstrengthindicator), AOA (angleofarrival), TOA (timeofarrival) and TDOA (timedifferenceonarrival) etc..

Method for positioning mass center based on RSSI technology, owing to having the advantages such as low implementation complexity, less hardware resource consumption, has being widely used in typical positioning system, such as cricket system.Although having relatively low implementation complexity, but it is constrained to the multipath transmisstion of signal, non line of sight (NoneLineofSight, the problem such as NLOS), coordinate counting accuracy is the highest, often there is the situation lost efficacy in location, bring bigger position error to tool, to such an extent as to the needs of indoor positioning in actual life cannot be adapted to.

As due to the interference of the noise such as multipath fading, barrier, often there is bigger fluctuation in signal strength values, the distance value error that RSSI value is changed out is bigger, cause the unknown node obtained based on RSSI distance-finding method to the distance deviation far away of beaconing nodes in actual range, intersecting two-by-two so that 3 circles cannot meet, weighted mass center location algorithm lost efficacy.

Summary of the invention

The technical problem to be solved is, can accurately carry out node locating in the case of providing one disjoint between circle and circle, improve the distance correction weighted mass center localization method optimized based on received signal strength of the fault-tolerant ability of location algorithm, adaptability and degree of accuracy in indoor environment.

The technical solution adopted in the present invention is: a kind of distance correction weighted mass center localization method optimized based on received signal strength, comprises the steps:

1) beaconing nodes periodically broadcasts self information, and described information includes: beaconing nodes ID, Power value, RSSI value and beaconing nodes positional information；

2) after unknown node receives information, the RSSI value that same beaconing nodes is got, use Gaussian mean mixed filtering model optimization strategy to be optimized screening, obtain accurate RSSI value；Obtain other beaconing nodes signal strength values to unknown node the most successively, ID and the Power value of beaconing nodes and the RSSI value after processing are deposited in the set R self maintained；

3) first the beaconing nodes RSSI value in set R is converted into range information by log path loss model by unknown node, and is ranked up from small to large by range information according to bubble sort method；Choose the value that front 4 distances are minimum again, set up beaconing nodes information aggregate D, including ID, beaconing nodes positional information and beaconing nodes and the range information of unknown node of beaconing nodes；

4) set D in appoint take three beaconing nodes as a combinationPosition, respectively with each beaconing nodes positional information as the center of circle, justify with the range information of corresponding beaconing nodes Yu unknown node for radius, judge whether intersect between circle with circle, if non-intersect use distance correction scheme carries out the correction of radius distance, obtain intersection point information, carry out, with the computing of the distance factor weighted mass center location algorithm as weights, obtaining four estimation positions of unknown node；

5) four meansigma methodss estimating position are sought, as the estimation positional information of unknown node.

Step 1) described in Power value be and received power value during base station distance 1m.

Step 2) described in use Gaussian mean mixed filtering model optimization strategy be optimized screening, including:

(1) to same beaconing nodes in same distance sampling RSSI value, carry out gaussian filtering, choose Gauss model distribution density and belong to the data value of RSSI ∈ [0.15 σ+μ, μ+3.09 σ] scope, by following gaussian filtering model filtering:

$f\left(x\right)=\frac{1}{\mathrm{\σ}\sqrt{2\mathrm{\π}}}{e}^{-\frac{{(x-\mathrm{\μ})}^2}{2{\mathrm{\σ}}^2}}---\left(1\right)$

Wherein:

${\mathrm{\σ}}^2=\frac{1}{n-1}\underset{i=1}{\overset{n}{\Σ}}{({\mathrm{RSSI}}_i-\mathrm{\μ})}^2---\left(3\right);$

(2) by mean filter optimisation strategy, virtual value remaining after gaussian filtering being carried out mean filter, after taking gaussian filtering, the arithmetic mean of instantaneous value of remaining virtual value is as final RSSI value.

Step 3) described in log path loss model as follows:

$RSSI={\mathrm{RSSI}}_0+10nlg\left(\frac{d}{d_0}\right)+\mathrm{\ϵ}---\left(5\right)$

Wherein, d₀For reference distance (generally 1m)；RSSI₀Be distance be d₀Time the signal intensity that receives；D is actual range；RSSI be distance for d time the signal intensity that receives；N is the radio signal attenuation factor closely-related with environment；ε be an average be the Gaussian random variable of zero.

Step 4) described in distance correction scheme be to be divided into two kinds of situations according between circle with circle without the position relationship of intersection point:

(1) two circle from

Exist between circle and circle two circles from situation time, i.e. two circles are without intersection point, and described distance correction scheme is: expand the radius certain distances of two circles respectively as weights using the distance factor, make round to intersect two-by-two with round, form an overlapping region；Re-use weighted mass center location algorithm and try to achieve unknown node coordinate；

(2) two circles include:

When presenting situation about including between two circles, described distance correction scheme is: uses and reduces the radius of the relatively large circle of radius, and the circle that the makes radius relatively large circle relatively small with radius is tangent or intersects, and there is common intersection between making two to justify.

Step 4) described in distance the factor weighted mass center location algorithm as weights be, it is known that three beaconing nodes are respectively as follows: O₁(x₁,y₁)、O₂(x₂,y₂)、O₃(x₃,y₃), D point is unknown node, and the range finding distance of D point to three beaconing nodes is r₁、r₂、r₃, according to distance and the mathematical model of coordinate of unknown node to beaconing nodes:

$\begin{array}{ccc}\left\{\begin{array}{c}x=r_1\×\cos \mathrm{\θ}+x_1\\ y=r_1\×\sin \mathrm{\θ}+y_1\end{array}\right.& \left\{\begin{array}{c}x=r_2\×\cos \mathrm{\θ}+x_2\\ y=r_2\×\sin \mathrm{\θ}+y_2\end{array}\right.& \left\{\begin{array}{c}x=r_3\×\cos \mathrm{\θ}+x_3\\ y=r_3\×\sin \mathrm{\θ}+y_3\end{array}\right.---\left(9\right)\end{array}$

Circle finds intersection two-by-two, obtains intersection point A (x_A,y_A)、B(x_B,y_B)、C(x_C,y_C), unknown node is just in the delta-shaped region that intersection point ABC is constituted, a weights ω is introduced in each location algorithm, described weights ω and distance related setting are the distance factor, utilize distance factor ω to embody the beaconing nodes influence degree to unknown node position, i.e. beaconing nodes is the most remote with unknown node, and during location estimation, proportion is the least, and each beaconing nodes is determined by two distances, therefore Weight selected isThus obtaining unknown node coordinate is:

$\left\{\begin{array}{c}x=\frac{(\frac{x_A}{r_2+r_3}+\frac{x_B}{r_1+r_3}+\frac{x_C}{r_1+r_2})}{(\frac{1}{r_2+r_3}+\frac{1}{r_1+r_3}+\frac{1}{r_1+r_2})}\\ y=\frac{(\frac{y_A}{r_2+r_3}+\frac{y_B}{r_1+r_3}+\frac{y_C}{r_1+r_2})}{(\frac{1}{r_2+r_3}+\frac{1}{r_1+r_3}+\frac{1}{r_1+r_2})}\end{array}\right.---\left(10\right).$

The distance correction weighted mass center localization method optimized based on received signal strength of the present invention, utilize the RSSI screening strategy of Gaussian mean mixed filtering, circle with circle between disjoint in the case of can accurately carry out node locating, the error that RSSI location algorithm is brought by environmental effect and random disturbances can be reduced, and use the weighting four limit centroid localization algorithm of distance correction to obtain more high position precision and more preferable fault-tolerant ability (i.e. weighted mass center location algorithm failure conditions) relative to traditional method, improve the adaptability of algorithm, can preferably be applied to indoor positioning.

Accompanying drawing explanation

Fig. 1 is triangular weighting centroid algorithm schematic diagram；

Fig. 2 a is weighted mass center algorithm two circle inefficacy schematic diagram without intersection point；

Fig. 2 b is for the inefficacy schematic diagram included mutually between weighted mass center algorithm two circle；

Fig. 3 is the distance correction weighted mass center location algorithm flow chart optimized based on received signal strength.

Detailed description of the invention

Below in conjunction with embodiment and accompanying drawing, the distance correction weighted mass center localization method based on received signal strength optimization of the present invention is described in detail.

First introducing range finding model based on signal intensity, it is a kind of signal intensity is converted into distance value to obtain theoretical model；Introduce the optimisation strategy that the signal processing for signal intensity is used afterwards, i.e. use Gaussian mean mixed filtering model to carry out RSSI value screening and optimizing；Finally provide the weighted mass center location algorithm of distance correction, utilize geometric barycenter as the positional information of unknown node.

RSSI finds range model

RSSI range measurement principle, is that wireless signal is decayed the most therewith with the increase transmitting signal intensity of distance.The present invention receives, by bluetooth 4.0 protocol stack, reference value POWER (with accepting performance number during base station distance 1m) and the RSSI value after wireless channel is decayed that Bluetooth beacon node sends, by wireless signal propagation model in indoor environment, calculate mobile terminal and the internodal distance of Bluetooth beacon.

The distance correction weighted mass center localization method optimized based on received signal strength of the present invention, as it is shown on figure 3, comprise the steps:

1) beaconing nodes periodically broadcasts self information, and described information includes: beaconing nodes ID, Power value, RSSI value and beaconing nodes positional information；Described Power value is and received power value during base station distance 1m, and RSSI (ReceivedSignalStrengthIndicator) is the intensity instruction receiving signal.

2) after unknown node receives information, the RSSI value that same beaconing nodes is got, use Gaussian mean mixed filtering model optimization strategy to be optimized screening, obtain accurate RSSI value；Obtain other beaconing nodes signal strength values to unknown node the most successively, ID and the Power value of beaconing nodes and the RSSI value after processing are deposited in the set R self maintained；

When utilizing RSSI to find range, due to the existence disturbed in environment and the problem of physical layer realization mechanism, part exceptional value can be there is in the RSSI value of the same distance of same beacon, these exceptional values can disturb positioning precision, by the RSSI signal processing optimisation strategy of Gaussian mean mixed filtering, filter the noise that extraneous fluctuation interference produces, get rid of the error that accidentalia is brought to experimental result, improve range accuracy, enhance the accuracy of location information.

Described use Gaussian mean mixed filtering model optimization strategy is optimized screening, including:

(1) to same beaconing nodes in same distance sampling RSSI value, carry out gaussian filtering, choose Gauss model distribution density and belong to the data value of RSSI ∈ [0.15 σ+μ, μ+3.09 σ] scope, by following gaussian filtering model filtering:

$f\left(x\right)=\frac{1}{\mathrm{\σ}\sqrt{2\mathrm{\π}}}{e}^{-\frac{{(x-\mathrm{\μ})}^2}{2{\mathrm{\σ}}^2}}---\left(1\right)$

Wherein:

${\mathrm{\σ}}^2=\frac{1}{n-1}\underset{i=1}{\overset{n}{\Σ}}{({\mathrm{RSSI}}_i-\mathrm{\μ})}^2---\left(3\right);$

(2) data away from actual value have been effectively filtered out through gaussian filtering, but remaining data still has certain fluctuation, cause positioning precision the most unstable, virtual value remaining after gaussian filtering is carried out mean filter by mean filter optimisation strategy to be passed through, after taking gaussian filtering, the arithmetic mean of instantaneous value of remaining virtual value is as final RSSI value, it is effectively increased reliability and the reasonability of RSSI range finding, for estimating that actual range is laid a good foundation further.RSSI value after correction is as follows:

$\stackrel{\‾}{RSSI}=\frac{1}{n}\underset{i=1}{\overset{n}{\Σ}}GuassRssi\[i\]---\left(4\right);$

3) first the beaconing nodes RSSI value in set R is converted into range information by log path loss model by unknown node, and is ranked up from small to large by range information according to bubble sort method；Choose the value that front 4 distances are minimum again, set up beaconing nodes information aggregate D, including ID, beaconing nodes positional information and beaconing nodes and the range information of unknown node of beaconing nodes；

Described log path loss model is as follows:

$RSSI={\mathrm{RSSI}}_0+10nlg\left(\frac{d}{d_0}\right)+\mathrm{\ϵ}---\left(5\right)$

Wherein, d₀For reference distance (generally 1m)；RSSI₀Be distance be d₀Time the signal intensity that receives；D is actual range；RSSI be distance for d time the signal intensity that receives；N is the radio signal attenuation factor closely-related with environment；ε be an average be the Gaussian random variable of zero,

Will obtaining after correctionValue is brought in log path loss model, i.e.:

$\stackrel{\‾}{RSSI}={\mathrm{RSSI}}_0+10nlg\left(\frac{d}{d_0}\right)+\mathrm{\ϵ}---\left(6\right)$

D can be got by beaconing nodes₀Received signal strength value Power value during=1m, brings in formula, i.e. tries to achieve distance d between beaconing nodes and unknown node.

4) when the inventive method utilizes received signal strength to position unknown node, internodal distance is obtained according to RSSI range finding model, unknown node filters out four nearest with unknown node beaconing nodes, using every for node therein three as a combination, utilize each integrated positioning unknown node, the result that each combination obtains is averaged, as the final elements of a fix.The present invention is when using four beaconing nodes nearest with unknown node to be weighted center coordination, it is judged that whether there is the situation without intersection point between circle and circle, this situation is carried out distance correction, makes four circles intersect two-by-two, form an overlapping region.Specifically:

Set D appoints and takes three beaconing nodes as a combinationPosition, respectively with each beaconing nodes positional information as the center of circle, justify with the range information of corresponding beaconing nodes Yu unknown node for radius, judge whether intersect between circle with circle, if non-intersect use distance correction scheme carries out the correction of radius distance, obtain intersection point information, carry out, with the computing of the distance factor weighted mass center location algorithm as weights, obtaining four estimation positions of unknown node；

Described distance correction scheme is to be divided into two kinds of situations according between circle with circle without the position relationship of intersection point:

(1) two circle from

Exist between circle and circle two circles from situation time, i.e. two is round without intersection point, as shown in Figure 2 a.Described distance correction scheme is: expand the radius certain distance of two circles respectively using the distance factor as weights, makes circle intersect two-by-two with circle, forms an overlapping region；The embodiment of the present invention uses following distance correction scheme, it is ensured that the radius ratio after increase and radius ratio before keep constant, i.e. weight shared by distance factor pair unknown node is constant.Re-use weighted mass center location algorithm and try to achieve unknown node coordinate；

It is as follows that radius increases formula:

$\left\{\begin{array}{c}{r}_1=\frac{r_2(d-r_1-r_2)}{r_1+r_2}+r_1\\ r_2=\frac{r_2(d-r_1-r_2)}{r_1+r_2}+r_2\end{array}\right.---\left(7\right)$

Wherein r₁、r₂It is two circle O₁、O₂Radius；D is two round heart distances.

(2) two circles include:

When presenting situation about including between two circles, as shown in Figure 2 b.Described distance correction scheme is: uses and reduces the radius of the relatively large circle of radius, and the circle that the makes radius relatively large circle relatively small with radius is tangent or intersects, and there is common intersection between making two to justify.The concrete amendment scheme that the embodiment of the present invention uses is as follows:

Wherein, r₁、r₂It is respectively circle O₁、O₂Radius, and r₁>r₂, d is two round heart distances.Use the method can ensure that two radius of circles are less than change on the basis of reducing two circle side-play amounts as far as possible, i.e. at corrected range so that in the case of it exists overlapping region so that it is distance is more minimum than change thus reduces distance change to the impact of weights in weighted mass center algorithm.

Described with the distance factor weighted mass center location algorithm as weights, it is that the error of the received signal strength utilizing signal propagation model to calculate realizes location, calculates, by RSSI value, the weights that unknown node is contributed by each beaconing nodes.Specific algorithm is: owing to RSSI value is easily affected by factors such as environmental disturbances and the irregular decay of electromagnetic field signal, error is certainly existed when changing into distance, therefore the perception of beaconing nodes is occured simultaneously is not a point, but a region, unknown node is just in this region.As shown in Figure 1, it is known that three beaconing nodes are respectively as follows: O₁(x₁,y₁)、O₂(x₂,y₂)、O₃(x₃,y₃), D point is unknown node, and the range finding distance of D point to three beaconing nodes is r₁、r₂、r₃, according to distance and the mathematical model of coordinate of unknown node to beaconing nodes:

$\begin{array}{ccc}\left\{\begin{array}{c}x=r_1\×\cos \mathrm{\θ}+x_1\\ y=r_1\×\sin \mathrm{\θ}+y_1\end{array}\right.& \left\{\begin{array}{c}x=r_2\×\cos \mathrm{\θ}+x_2\\ y=r_2\×\sin \mathrm{\θ}+y_2\end{array}\right.& \left\{\begin{array}{c}x=r_3\×\cos \mathrm{\θ}+x_3\\ y=r_3\×\sin \mathrm{\θ}+y_3\end{array}\right.---\left(9\right)\end{array}$

Circle finds intersection two-by-two, obtains intersection point A (x_A,y_A)、B(x_B,y_B)、C(x_C,y_C), unknown node is just in the delta-shaped region that intersection point ABC is constituted, introducing a weights ω in each location algorithm prevents information from flooding phenomenon (influence factor that i.e. center-of-mass coordinate is estimated by the relevant information of beaconing nodes), described weights ω and distance related setting are the distance factor, distance factor ω is utilized to embody the beaconing nodes influence degree to unknown node position, i.e. beaconing nodes is the most remote with unknown node, during location estimation, proportion is the least, and each beaconing nodes is determined by two distances, therefore Weight selected is(to justify O₁As a example by, r₂、r₃For circle O₂、O₃Radius), thus obtaining unknown node coordinate is:

$\left\{\begin{array}{c}x=\frac{(\frac{x_A}{r_2+r_3}+\frac{x_B}{r_1+r_3}+\frac{x_C}{r_1+r_2})}{(\frac{1}{r_2+r_3}+\frac{1}{r_1+r_3}+\frac{1}{r_1+r_2})}\\ y=\frac{(\frac{y_A}{r_2+r_3}+\frac{y_B}{r_1+r_3}+\frac{y_C}{r_1+r_2})}{(\frac{1}{r_2+r_3}+\frac{1}{r_1+r_3}+\frac{1}{r_1+r_2})}\end{array}\right.---\left(10\right).$

5) four meansigma methodss estimating position are sought, as the estimation positional information of unknown node.

It is above the explanation of the distance correction weighted mass center localization method optimized based on received signal strength to the present invention.Can be seen that from described above, present invention is generally directed to the network of random distribution, on the basis of traditional triangle shape weighted mass center location algorithm, consider the situation causing location algorithm to lose efficacy because of RSSI range error, it is proposed that a kind of new RSSI optimisation strategy and the weighted mass center algorithm of distance correction scheme.

It should be noted last that, above example is only in order to illustrate technical scheme and unrestricted.Although the present invention being described in detail with reference to embodiment, it will be understood by those within the art that, modifying technical scheme or equivalent, without departure from the spirit and scope of technical solution of the present invention, it all should be contained in the middle of scope of the presently claimed invention.

Claims (6)

1. the distance correction weighted mass center localization method optimized based on received signal strength, it is characterised in that comprise the steps:

1) beaconing nodes periodically broadcasts self information, and described information includes: beaconing nodes ID, Power value, RSSI value and beaconing nodes positional information；

2) after unknown node receives information, the RSSI value that same beaconing nodes is got, use Gaussian mean mixed filtering model optimization strategy to be optimized screening, obtain accurate RSSI value；Obtain other beaconing nodes signal strength values to unknown node the most successively, ID and the Power value of beaconing nodes and the RSSI value after processing are deposited in the set R self maintained；

3) first the beaconing nodes RSSI value in set R is converted into range information by log path loss model by unknown node, and is ranked up from small to large by range information according to bubble sort method；Choose the value that front 4 distances are minimum again, set up beaconing nodes information aggregate D, including ID, beaconing nodes positional information and beaconing nodes and the range information of unknown node of beaconing nodes；

4) set D in appoint take three beaconing nodes as a combinationPosition, respectively with each beaconing nodes positional information as the center of circle, justify with the range information of corresponding beaconing nodes Yu unknown node for radius, judge whether intersect between circle with circle, if non-intersect use distance correction scheme carries out the correction of radius distance, obtain intersection point information, carry out, with the computing of the distance factor weighted mass center location algorithm as weights, obtaining four estimation positions of unknown node；

5) four meansigma methodss estimating position are sought, as the estimation positional information of unknown node.

The most according to claim 1 based on received signal strength optimize distance correction weighted mass center localization method, it is characterised in that step 1) described in Power value be and received power value during base station distance 1m.

The most according to claim 1 based on received signal strength optimize distance correction weighted mass center localization method, it is characterised in that step 2) described in use Gaussian mean mixed filtering model optimization strategy be optimized screening, including:

(1) to same beaconing nodes in same distance sampling RSSI value, carry out gaussian filtering, choose Gauss model distribution density and belong to the data value of RSSI ∈ [0.15 σ+μ, μ+3.09 σ] scope, by following gaussian filtering model filtering:

$f\left(x\right)=\frac{1}{\mathrm{\σ}\sqrt{2\mathrm{\π}}}{e}^{-\frac{{(x-\mathrm{\μ})}^2}{2{\mathrm{\σ}}^2}}---\left(1\right)$

Wherein:

${\mathrm{\σ}}^2=\frac{1}{n-1}\underset{i=1}{\overset{n}{\Σ}}{({\mathrm{RSSI}}_i-\mathrm{\μ})}^2---\left(3\right);$

(2) by mean filter optimisation strategy, virtual value remaining after gaussian filtering being carried out mean filter, after taking gaussian filtering, the arithmetic mean of instantaneous value of remaining virtual value is as final RSSI value.

The most according to claim 1 based on received signal strength optimize distance correction weighted mass center localization method, it is characterised in that step 3) described in log path loss model as follows:

$RSSI={\mathrm{RSSI}}_0+10n\lg \left(\frac{d}{d_0}\right)+\mathrm{\ϵ}---\left(5\right)$

Wherein, d₀For reference distance (generally 1m)；RSSI₀Be distance be d₀Time the signal intensity that receives；D is actual range；RSSI be distance for d time the signal intensity that receives；N is the radio signal attenuation factor closely-related with environment；ε be an average be the Gaussian random variable of zero.

The distance correction weighted mass center localization method optimized based on received signal strength the most according to claim 1, it is characterised in that step 4) described in distance correction scheme be to be divided into two kinds of situations according between circle with circle without the position relationship of intersection point:

(1) two circle from

Exist between circle and circle two circles from situation time, i.e. two circles are without intersection point, and described distance correction scheme is: expand the radius certain distances of two circles respectively as weights using the distance factor, make round to intersect two-by-two with round, form an overlapping region；Re-use weighted mass center location algorithm and try to achieve unknown node coordinate；

(2) two circles include:

When presenting situation about including between two circles, described distance correction scheme is: uses and reduces the radius of the relatively large circle of radius, and the circle that the makes radius relatively large circle relatively small with radius is tangent or intersects, and there is common intersection between making two to justify.

The most according to claim 1 based on received signal strength optimize distance correction weighted mass center localization method, it is characterised in that step 4) described in distance the factor weighted mass center location algorithm as weights be, it is known that three beaconing nodes are respectively as follows: O₁(x₁,y₁)、O₂(x₂,y₂)、O₃(x₃,y₃), D point is unknown node, and the range finding distance of D point to three beaconing nodes is r₁、r₂、r₃, according to distance and the mathematical model of coordinate of unknown node to beaconing nodes:

$\begin{array}{ccc}\left\{\begin{array}{c}x=r_1\×cos\mathrm{\θ}+x_1\\ y=r_1\×\sin \mathrm{\θ}+y_1\end{array}\right.& \left\{\begin{array}{c}x=r_2\×cos\mathrm{\θ}+x_2\\ y=r_2\×\sin \mathrm{\θ}+y_2\end{array}\right.& \left\{\begin{array}{c}x=r_3\×cos\mathrm{\θ}+x_3\\ y=r_3\×\sin \mathrm{\θ}+y_3\end{array}\right.\end{array}---\left(9\right)$

Circle finds intersection two-by-two, obtains intersection point A (x_A,y_A)、B(x_B,y_B)、C(x_C,y_C), unknown node, just in the delta-shaped region that intersection point ABC is constituted, introduces a weights ω, described weights ω in each location algorithm.It is the distance factor with distance related setting, utilizes distance factor ω.Embody the beaconing nodes influence degree to unknown node position, i.e. beaconing nodes is the most remote with unknown node, and during location estimation, proportion is the least, and each beaconing nodes is determined by two distances, therefore Weight selected isThus obtaining unknown node coordinate is:

$\left\{\begin{array}{c}x=\frac{\left(\frac{x_A}{r_2+r_3}+\frac{x_B}{r_1+r_3}+\frac{x_C}{r_1+r_2}\right)}{\left(\frac{1}{r_2+r_3}+\frac{1}{r_1+r_3}+\frac{1}{r_1+r_2}\right)}\\ y=\frac{\left(\frac{y_A}{r_2+r_3}+\frac{y_B}{r_1+r_3}+\frac{y_C}{r_1+r_2}\right)}{\left(\frac{1}{r_2+r_3}+\frac{1}{r_1+r_3}+\frac{1}{r_1+r_2}\right)}\end{array}\right.---\left(10\right).$